hypothesis reasoning example

How to Write a Hypothesis [31 Tips + Examples]

Writing hypotheses can seem tricky, but it’s essential for a solid scientific inquiry.

Here is a quick summary of how to write a hypothesis:

Write a hypothesis by clearly defining your research question, identifying independent and dependent variables, formulating a measurable prediction, and ensuring it can be tested through experimentation. Include an “if…then” statement for clarity.

I’ve crafted dozens in my research, from basic biology experiments to business marketing strategies.

Let me walk you through how to write a solid hypothesis, step by step.

Writing a Hypothesis: The Basics

Notebook and scientific diagrams glow amidst dramatic lighting -- How to Write a Hypothesis

Table of Contents

A hypothesis is a statement predicting the relationship between variables based on observations and existing knowledge. To craft a good hypothesis:

Identify variables – Determine the independent and dependent variables involved.
Predict relationships – Predict the interaction between these variables.
Test the statement – Ensure the hypothesis is testable and falsifiable.

A solid hypothesis guides your research and sets the foundation for your experiment.

31 Tips for Writing a Hypothesis

There are at least 31 tips to write a good hypothesis.

Keep reading to learn every tip plus three examples to make sure that you can instantly apply it to your writing.

Tip 1: Start with a Clear Research Question

A clear research question ensures your hypothesis is targeted.

Identify the broad topic you’re curious about, then refine it to a specific question.
Use guiding questions like “What impact does variable X have on variable Y?”
How does fertilizer affect plant growth?
Does social media influence mental health in teens?
Can personalized ads increase customer engagement?

Tip 2: Do Background Research

Research helps you understand current knowledge and any existing gaps.

Review scholarly articles, reputable websites, and textbooks.
Focus on understanding the relationships between variables in existing research.
Academic journals like ScienceDirect or JSTOR.
Google Scholar.
Reputable news articles.

Tip 3: Identify Independent and Dependent Variables

The independent variable is what you change or control. The dependent variable is what you measure.

Clearly define these variables to make your hypothesis precise.
Think of different factors that could be influencing your dependent variable.
Type of fertilizer (independent) and plant growth (dependent).
Amount of screen time (independent) and anxiety levels (dependent).
Marketing strategies (independent) and customer engagement (dependent).

Tip 4: Make Your Hypothesis Testable

A hypothesis must be measurable and falsifiable.

Ensure your hypothesis can be supported or refuted through data collection.
Include numerical variables or qualitative changes to ensure measurability.
“Increasing screen time will increase anxiety levels in teenagers.”
“Using fertilizer X will yield higher crop productivity.”
“A/B testing marketing strategies will show higher engagement with personalized ads.”

Tip 5: Be Specific and Concise

Keep your hypothesis straightforward and to the point.

Avoid vague terms that could mislead or cause confusion.
Clearly outline what you’re measuring and how the variables interact.
“Replacing chemical fertilizers with organic ones will result in slower plant growth.”
“A social media break will decrease anxiety in high school students.”
“Ads targeting user preferences will boost click-through rates by 10%.”

Tip 6: Choose Simple Language

Use simple, understandable language to ensure clarity.

Avoid jargon and overly complex terms that could confuse readers.
Make the hypothesis comprehensible to non-experts in the field.
“Organic fertilizer will reduce plant growth.”
“High schoolers will feel less anxious after a social media detox.”
“Targeted ads will increase customer engagement.”

Tip 7: Formulate a Null Hypothesis

A null hypothesis assumes no relationship between variables.

Create a counterpoint to your main hypothesis, asserting that there is no effect.
This allows you to compare results directly and identify statistical significance.
“Fertilizer type will not affect plant growth.”
“Social media use will not influence anxiety.”
“Targeted ads will not affect customer engagement.”

Tip 8: State Alternative Hypotheses

Provide alternative hypotheses to explore other plausible relationships.

They offer a contingency plan if your primary hypothesis is not supported.
These should still align with your research question and measurable variables.
“Fertilizer X will only affect plant growth if used in specific soil types.”
“Social media might impact anxiety only in certain age groups.”
“Customer engagement might only improve with highly personalized ads.”

Tip 9: Use “If…Then” Statements

“If…then” statements simplify the cause-and-effect structure.

The “if” clause identifies the independent variable, while “then” identifies the dependent.
It makes your hypothesis easier to understand and directly testable.
“If plants receive organic fertilizer, then their growth rate will slow.”
“If teens stop using social media, then their anxiety will decrease.”
“If ads are personalized, then click-through rates will increase.”

Tip 10: Avoid Assumptions

Don’t assume the audience understands your variables or relationships.

Clearly define terms and relationships to avoid misinterpretation.
Provide background context where necessary for clarity.
Define “anxiety” as a feeling of worry or unease.
Specify “plant growth” as the height and health of plants.
Describe “personalized ads” as ads matching user preferences.

Tip 11: Review Existing Literature

Previous research offers insights into forming a hypothesis.

Conduct a thorough literature review to identify trends and gaps.
Use these studies to refine and build upon your hypothesis.
Studies showing a link between screen time and anxiety.
Research on organic versus chemical fertilizers.
Customer behavior analysis in different marketing channels.

Tip 12: Consider Multiple Variables

Hypotheses with multiple variables can offer deeper insights.

Explore combinations of independent and dependent variables to see their relationships.
Plan experiments accordingly to distinguish separate effects.
Studying fertilizer type and soil composition effects on plant growth.
Testing social media use frequency and content type on anxiety.
Analyzing marketing strategies combined with product preferences.

Tip 13: Review Ethical Considerations

Ethics are essential for trustworthy research.

Avoid hypotheses that could cause harm to participants or the environment.
Seek approval from relevant ethical boards or committees.
Avoiding experiments causing undue stress to teenagers.
Preventing chemical contamination when testing fertilizers.
Respecting privacy with personalized ads.

Tip 14: Test with Pilot Studies

Small-scale pilot studies test feasibility and refine hypotheses.

Use them to identify potential issues and adjust before full-scale research.
Ensure pilot tests align with ethical standards.
Testing different fertilizer types on small plant samples.
Trying brief social media breaks with a small group of teens.
Conducting A/B tests on ad personalization with a subset of customers.

Tip 15: Build Hypotheses on Existing Theories

Existing theories provide strong foundations.

Use established frameworks to develop or refine your hypothesis.
Testing theoretical predictions can yield meaningful data.
Applying agricultural theories on soil and crop management.
Using psychology theories on screen addiction and mental health.
Referencing marketing theories like consumer behavior analysis.

Tip 16: Address Real-World Problems

Solve real-world problems through practical hypotheses.

Make sure your research question has relevant, impactful applications.
Focus on everyday challenges where actionable insights can help.
Testing new eco-friendly farming methods.
Reducing anxiety by improving digital wellbeing.
Improving marketing ROI with personalized strategies.

Tip 17: Aim for Clear, Measurable Outcomes

The results should be easy to measure and interpret.

Quantify your dependent variable or use defined qualitative measures.
Avoid overly broad or ambiguous outcomes.
Measuring plant growth as a percentage change in height.
Quantifying anxiety levels through standard surveys.
Tracking click-through rates as a percentage of total views.

Tip 18: Stay Open to Unexpected Results

Not all hypotheses yield expected results.

Be open to learning new insights, even if they contradict your prediction.
Unexpected findings often reveal unique, significant knowledge.
Unexpected fertilizer types boosting growth differently than anticipated.
Screen time affecting anxiety differently across various age groups.
Targeted ads backfiring with specific customer segments.

Tip 19: Keep Hypotheses Relevant

Ensure your hypothesis aligns with the purpose of your research.

Avoid straying from the original question or focusing on tangential issues.
Stick to the research scope to ensure accurate and meaningful data.
Focus on a specific type of fertilizer for plant growth.
Restrict studies to relevant age groups for anxiety research.
Keep marketing hypotheses within the same target customer segment.

Tip 20: Collaborate with Peers

Collaboration strengthens hypothesis development.

Work with colleagues or mentors for valuable feedback.
Peer review helps identify flaws or assumptions in your hypothesis.
Reviewing hypothesis clarity with a lab partner.
Sharing research plans with a mentor to refine focus.
Engaging in academic peer-review groups.

Tip 21: Re-evaluate Hypotheses Periodically

Revising hypotheses ensures relevance.

Update based on new literature, data, or technological advances.
A dynamic approach keeps your research current.
Refining fertilizer studies with recent organic farming research.
Adjusting social media hypotheses for new platforms like TikTok.
Modifying marketing hypotheses based on changing customer preferences.

Tip 22: Develop Compelling Visuals

Illustrating hypotheses can help communicate relationships effectively.

Use diagrams or flowcharts to show how variables interact visually.
Infographics make it easier for others to grasp your research concept.
A flowchart showing fertilizer effects on different plant growth stages.
Diagrams illustrating social media use and its psychological impact.
Infographics depicting how various marketing strategies boost engagement.

Tip 23: Refine Your Data Collection Plan

A solid data collection plan is vital for a testable hypothesis.

Determine the best ways to measure your dependent variable.
Ensure your data collection tools are reliable and accurate.
Using a ruler and image analysis software to measure plant height.
Designing standardized surveys to assess anxiety levels consistently.
Setting up click-through tracking with analytics software.

Tip 24: Focus on Logical Progression

Ensure your hypothesis logically follows your research question.

The relationship between variables should naturally flow from your observations.
Avoid logical leaps that might confuse your reasoning.
Predicting plant growth after observing effects of different fertilizers.
Linking anxiety to social media use based on screen time studies.
Connecting ad personalization with customer behavior data.

Tip 25: Test Against Diverse Samples

Testing across diverse samples ensures broader applicability.

Avoid drawing conclusions from overly narrow sample groups.
Try to include different demographics or subgroups in your testing.
Testing fertilizer effects on multiple plant species.
Including different age groups in anxiety research.
Experimenting with personalized ads across varied customer segments.

Tip 26: Use Control Groups

Control groups provide a baseline for comparison.

Compare your test group with a control group under unchanged conditions.
This allows you to isolate the effect of your independent variable.
Comparing plant growth with organic versus no fertilizer.
Testing anxiety levels with and without social media breaks.
Comparing personalized ads with general marketing content.

Tip 27: Consider Practical Constraints

Work within realistic constraints for your resources and timeline.

Assess the feasibility of testing your hypothesis.
Modify the hypothesis if the required testing is unmanageable.
Reducing fertilizer types to a manageable number for testing.
Shortening social media detox periods to realistic durations.
Targeting only specific marketing strategies to optimize testing.

Tip 28: Recognize Bias Risks

Biases can skew hypothesis formation.

Acknowledge your assumptions and how they may affect your research.
Minimize biases by clearly defining and measuring variables.
Avoiding assumptions that organic fertilizer is inherently better.
Ensuring survey questions don’t lead to specific anxiety outcomes.
Testing marketing strategies objectively without favoring any method.

Tip 29: Prepare for Peer Review

Peer review ensures your hypothesis holds up to scrutiny.

Provide a clear rationale for why your hypothesis is sound.
Address potential criticisms to strengthen your research.
Showing your plant growth study builds on existing fertilizer research.
Demonstrating social media anxiety links through data and literature.
Supporting your marketing hypotheses with solid behavioral data.

Tip 30: Create a Research Proposal

A proposal outlines your hypothesis, methodology, and significance.

It ensures your hypothesis is clear and your methods are well-thought-out.
Proposals also help secure funding or institutional approval.
A proposal for fertilizer studies linking plant growth and soil health.
Research plans connecting social media habits to anxiety measures.
Marketing proposals tying customer behavior to personalized advertising.

Tip 31: Document Your Findings

Recording findings helps validate or challenge your hypothesis.

Document the methodology, data, and conclusions clearly.
This allows others to verify, replicate, or expand on your work.
Recording fertilizer effects on plant height in different soil types.
Survey results linking social media use with anxiety levels.
Click-through data proving personalized ads’ impact on engagement.

Check out this really good video about how to write a hypothesis:

Hypothesis Examples for Different Situations

Let’s look at some examples of how to write a hypothesis in different circumstances.

Marketing Analysis : “If personalized ads are shown to our target demographic, then click-through rates will increase by at least 10%.”
Process Improvement : “If automated workflows replace manual data entry, then task completion times will decrease by 20%.”
Product Development : “If adding a chatbot feature to our app increases customer support efficiency, then user satisfaction will improve by 15%.”
Biology Experiment : “If students grow plants with different fertilizers, then the organic fertilizer will result in slower growth compared to the chemical fertilizer.”
Psychology Research : “If high school students take a break from social media, then their levels of anxiety will decrease.”
Environmental Study : “If a controlled forest area is exposed to a certain pollutant, then the local plant species will show signs of damage within two weeks.”

Professional Contacts

Medical Research : “If a novel treatment method is applied to patients with chronic illness, then their recovery rate will increase significantly compared to standard treatment.”
Technology Research : “If machine learning algorithms analyze big data sets, then the accuracy of predictive models will surpass traditional data analysis.”
Engineering Project : “If new composite materials replace standard components in bridge construction, then the resulting structure will be more durable.”

Super Personal

Gardening Experiment : “If different types of compost are used in home gardens, then plants receiving homemade compost will yield the most produce.”
Fitness Routine : “If consistent strength training is combined with a high-protein diet, then muscle mass will increase more than with diet alone.”
Cooking Techniques : “If searing is added before baking, then the resulting roast will retain more moisture.”

Final Thoughts: How to Write a Hypothesis

Crafting hypotheses is both a science and an art. It’s about channeling curiosity into testable questions that propel meaningful discovery.

Each well-thought-out hypothesis is a stepping stone that could lead to the breakthrough you’ve been seeking.

Stay curious and let your research journey unfold.

How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk, "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

Operationalization

Hypothesis Types

Hypotheses examples.

Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

Forming a question
Performing background research
Creating a hypothesis
Designing an experiment
Collecting data
Analyzing the results
Drawing conclusions
Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you expect to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

Is your hypothesis based on your research on a topic?
Can your hypothesis be tested?
Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

Collect as many observations about a topic or problem as you can.
Evaluate these observations and look for possible causes of the problem.
Create a list of possible explanations that you might want to explore.
After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method , falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that if something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

Does your hypothesis focus on something that you can actually test?
Does your hypothesis include both an independent and dependent variable?
Can you manipulate the variables?
Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the dependent variable if you change the independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

"Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
"Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."
"Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
"Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

"People with high-sugar diets and sedentary activity levels are more likely to develop depression."
"Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

"There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
"There is no difference in scores on a memory recall task between children and adults."
"There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

"People who take St. John's wort supplements will have less anxiety than those who do not."
"Adults will perform better on a memory task than children."
"Children who play first-person shooter games will show higher levels of aggression than children who do not."

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as case studies , naturalistic observations , and surveys are often used when conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually cause another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses . R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:]. Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ? PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies . Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Educational resources and simple solutions for your research journey

Research hypothesis: What it is, how to write it, types, and examples

What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .

Table of Contents

What is a hypothesis ?

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.

What is a research hypothesis ?

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.

Characteristics of a good hypothesis

Here are the characteristics of a good hypothesis :

Clearly formulated and free of language errors and ambiguity
Concise and not unnecessarily verbose
Has clearly defined variables
Testable and stated in a way that allows for it to be disproven
Can be tested using a research design that is feasible, ethical, and practical
Specific and relevant to the research problem
Rooted in a thorough literature search
Can generate new knowledge or understanding.

How to create an effective research hypothesis

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.

Let’s look at each step for creating an effective, testable, and good research hypothesis :

Identify a research problem or question: Start by identifying a specific research problem.
Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.
Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.
State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.
Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.
Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.

How to write a research hypothesis

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.

An example of a research hypothesis in this format is as follows:

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”

Population: athletes

Independent variable: daily cold water showers

Dependent variable: endurance

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.

Research hypothesis checklist

Following from above, here is a 10-point checklist for a good research hypothesis :

Testable: A research hypothesis should be able to be tested via experimentation or observation.
Specific: A research hypothesis should clearly state the relationship between the variables being studied.
Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.
Falsifiable: A research hypothesis should be able to be disproven through testing.
Clear and concise: A research hypothesis should be stated in a clear and concise manner.
Logical: A research hypothesis should be logical and consistent with current understanding of the subject.
Relevant: A research hypothesis should be relevant to the research question and objectives.
Feasible: A research hypothesis should be feasible to test within the scope of the study.
Reflects the population: A research hypothesis should consider the population or sample being studied.
Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.

Types of research hypothesis

Different types of research hypothesis are used in scientific research:

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.

Example: “ The newly identified virus is not zoonotic .”

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.

Example: “ The newly identified virus is zoonotic .”

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.

Example, “ Cats and dogs differ in the amount of affection they express .”

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.

Example: “ Applying sunscreen every day slows skin aging .”

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)

7. Associative hypothesis:

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.

Example: “ There is a positive association between physical activity levels and overall health .”

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.

Example: “ Long-term alcohol use causes liver damage .”

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.

Research hypothesis examples

Here are some good research hypothesis examples :

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)

Importance of testable hypothesis

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.

Frequently Asked Questions (FAQs) on research hypothesis

1. What is the difference between research question and research hypothesis ?

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.

3. How can I be sure my hypothesis is testable?

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:

Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.
The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.
You should be able to collect the necessary data within the constraints of your study.
It should be possible for other researchers to replicate your study, using the same methods and variables.
Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.
The hypothesis should be able to be disproven or rejected through the collection of data.

4. How do I revise my research hypothesis if my data does not support it?

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.

5. I am performing exploratory research. Do I need to formulate a research hypothesis?

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a or H 1 ).
Collect data in a way designed to test the hypothesis.
Perform an appropriate statistical test .
Decide whether to reject or fail to reject your null hypothesis.
Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

an estimate of the difference in average height between the two groups.
a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Normal distribution
Descriptive statistics
Measures of central tendency
Correlation coefficient

Methodology

Cluster sampling
Stratified sampling
Types of interviews
Cohort study
Thematic analysis

Research bias

Implicit bias
Cognitive bias
Survivorship bias
Availability heuristic
Nonresponse bias
Regression to the mean

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

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On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

Research Methodology

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Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

How to Write a Hypothesis: A Step-by-Step Guide

Introduction

An overview of the research hypothesis, different types of hypotheses, variables in a hypothesis, how to formulate an effective research hypothesis, designing a study around your hypothesis.

The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. Let's look more closely at the idea of the hypothesis and the role it plays in research.

As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. A good research hypothesis is informed by prior research and guides research design and data analysis , so it is important to understand how a hypothesis is defined and understood by researchers.

What is the simple definition of a hypothesis?

A hypothesis is a testable prediction about an outcome between two or more variables . It functions as a navigational tool in the research process, directing what you aim to predict and how.

What is the hypothesis for in research?

In research, a hypothesis serves as the cornerstone for your empirical study. It not only lays out what you aim to investigate but also provides a structured approach for your data collection and analysis.

Essentially, it bridges the gap between the theoretical and the empirical, guiding your investigation throughout its course.

What is an example of a hypothesis?

If you are studying the relationship between physical exercise and mental health, a suitable hypothesis could be: "Regular physical exercise leads to improved mental well-being among adults."

This statement constitutes a specific and testable hypothesis that directly relates to the variables you are investigating.

What makes a good hypothesis?

A good hypothesis possesses several key characteristics. Firstly, it must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Secondly, a hypothesis should be specific and unambiguous, giving a clear understanding of the expected relationship between variables. Lastly, it should be grounded in existing research or theoretical frameworks , ensuring its relevance and applicability.

Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve the essential function of guiding your study, there are varying purposes among the types of hypotheses. In addition, all hypotheses stand in contrast to the null hypothesis, or the assumption that there is no significant relationship between the variables .

Here, we explore various kinds of hypotheses to provide you with the tools needed to craft effective hypotheses for your specific research needs. Bear in mind that many of these hypothesis types may overlap with one another, and the specific type that is typically used will likely depend on the area of research and methodology you are following.

Null hypothesis

The null hypothesis is a statement that there is no effect or relationship between the variables being studied. In statistical terms, it serves as the default assumption that any observed differences are due to random chance.

For example, if you're studying the effect of a drug on blood pressure, the null hypothesis might state that the drug has no effect.

Alternative hypothesis

Contrary to the null hypothesis, the alternative hypothesis suggests that there is a significant relationship or effect between variables.

Using the drug example, the alternative hypothesis would posit that the drug does indeed affect blood pressure. This is what researchers aim to prove.

Simple hypothesis

A simple hypothesis makes a prediction about the relationship between two variables, and only two variables.

For example, "Increased study time results in better exam scores." Here, "study time" and "exam scores" are the only variables involved.

Complex hypothesis

A complex hypothesis, as the name suggests, involves more than two variables. For instance, "Increased study time and access to resources result in better exam scores." Here, "study time," "access to resources," and "exam scores" are all variables.

This hypothesis refers to multiple potential mediating variables. Other hypotheses could also include predictions about variables that moderate the relationship between the independent variable and dependent variable .

Directional hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. For example, "Eating more fruits and vegetables leads to a decrease in heart disease."

Here, the direction of heart disease is explicitly predicted to decrease, due to effects from eating more fruits and vegetables. All hypotheses typically specify the expected direction of the relationship between the independent and dependent variable, such that researchers can test if this prediction holds in their data analysis .

Statistical hypothesis

A statistical hypothesis is one that is testable through statistical methods, providing a numerical value that can be analyzed. This is commonly seen in quantitative research .

For example, "There is a statistically significant difference in test scores between students who study for one hour and those who study for two."

Empirical hypothesis

An empirical hypothesis is derived from observations and is tested through empirical methods, often through experimentation or survey data . Empirical hypotheses may also be assessed with statistical analyses.

For example, "Regular exercise is correlated with a lower incidence of depression," could be tested through surveys that measure exercise frequency and depression levels.

Causal hypothesis

A causal hypothesis proposes that one variable causes a change in another. This type of hypothesis is often tested through controlled experiments.

For example, "Smoking causes lung cancer," assumes a direct causal relationship.

Associative hypothesis

Unlike causal hypotheses, associative hypotheses suggest a relationship between variables but do not imply causation.

For instance, "People who smoke are more likely to get lung cancer," notes an association but doesn't claim that smoking causes lung cancer directly.

Relational hypothesis

A relational hypothesis explores the relationship between two or more variables but doesn't specify the nature of the relationship.

For example, "There is a relationship between diet and heart health," leaves the nature of the relationship (causal, associative, etc.) open to interpretation.

Logical hypothesis

A logical hypothesis is based on sound reasoning and logical principles. It's often used in theoretical research to explore abstract concepts, rather than being based on empirical data.

For example, "If all men are mortal and Socrates is a man, then Socrates is mortal," employs logical reasoning to make its point.

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In any research hypothesis, variables play a critical role. These are the elements or factors that the researcher manipulates, controls, or measures. Understanding variables is essential for crafting a clear, testable hypothesis and for the stages of research that follow, such as data collection and analysis.

In the realm of hypotheses, there are generally two types of variables to consider: independent and dependent. Independent variables are what you, as the researcher, manipulate or change in your study. It's considered the cause in the relationship you're investigating. For instance, in a study examining the impact of sleep duration on academic performance, the independent variable would be the amount of sleep participants get.

Conversely, the dependent variable is the outcome you measure to gauge the effect of your manipulation. It's the effect in the cause-and-effect relationship. The dependent variable thus refers to the main outcome of interest in your study. In the same sleep study example, the academic performance, perhaps measured by exam scores or GPA, would be the dependent variable.

Beyond these two primary types, you might also encounter control variables. These are variables that could potentially influence the outcome and are therefore kept constant to isolate the relationship between the independent and dependent variables . For example, in the sleep and academic performance study, control variables could include age, diet, or even the subject of study.

By clearly identifying and understanding the roles of these variables in your hypothesis, you set the stage for a methodologically sound research project. It helps you develop focused research questions, design appropriate experiments or observations, and carry out meaningful data analysis . It's a step that lays the groundwork for the success of your entire study.

Crafting a strong, testable hypothesis is crucial for the success of any research project. It sets the stage for everything from your study design to data collection and analysis . Below are some key considerations to keep in mind when formulating your hypothesis:

Be specific : A vague hypothesis can lead to ambiguous results and interpretations . Clearly define your variables and the expected relationship between them.
Ensure testability : A good hypothesis should be testable through empirical means, whether by observation , experimentation, or other forms of data analysis.
Ground in literature : Before creating your hypothesis, consult existing research and theories. This not only helps you identify gaps in current knowledge but also gives you valuable context and credibility for crafting your hypothesis.
Use simple language : While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording.
State direction, if applicable : If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this. You also need to think about how you will measure whether or not the outcome moved in the direction you predicted.
Keep it focused : One of the common pitfalls in hypothesis formulation is trying to answer too many questions at once. Keep your hypothesis focused on a specific issue or relationship.
Account for control variables : Identify any variables that could potentially impact the outcome and consider how you will control for them in your study.
Be ethical : Make sure your hypothesis and the methods for testing it comply with ethical standards , particularly if your research involves human or animal subjects.

Designing your study involves multiple key phases that help ensure the rigor and validity of your research. Here we discuss these crucial components in more detail.

Literature review

Starting with a comprehensive literature review is essential. This step allows you to understand the existing body of knowledge related to your hypothesis and helps you identify gaps that your research could fill. Your research should aim to contribute some novel understanding to existing literature, and your hypotheses can reflect this. A literature review also provides valuable insights into how similar research projects were executed, thereby helping you fine-tune your own approach.

Research methods

Choosing the right research methods is critical. Whether it's a survey, an experiment, or observational study, the methodology should be the most appropriate for testing your hypothesis. Your choice of methods will also depend on whether your research is quantitative, qualitative, or mixed-methods. Make sure the chosen methods align well with the variables you are studying and the type of data you need.

Preliminary research

Before diving into a full-scale study, it’s often beneficial to conduct preliminary research or a pilot study . This allows you to test your research methods on a smaller scale, refine your tools, and identify any potential issues. For instance, a pilot survey can help you determine if your questions are clear and if the survey effectively captures the data you need. This step can save you both time and resources in the long run.

Data analysis

Finally, planning your data analysis in advance is crucial for a successful study. Decide which statistical or analytical tools are most suited for your data type and research questions . For quantitative research, you might opt for t-tests, ANOVA, or regression analyses. For qualitative research , thematic analysis or grounded theory may be more appropriate. This phase is integral for interpreting your results and drawing meaningful conclusions in relation to your research question.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
Education : “Implementing a new teaching method will result in higher student achievement scores.”
Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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General Education

Think about something strange and unexplainable in your life. Maybe you get a headache right before it rains, or maybe you think your favorite sports team wins when you wear a certain color. If you wanted to see whether these are just coincidences or scientific fact, you would form a hypothesis, then create an experiment to see whether that hypothesis is true or not.

But what is a hypothesis, anyway? If you’re not sure about what a hypothesis is--or how to test for one!--you’re in the right place. This article will teach you everything you need to know about hypotheses, including:

Defining the term “hypothesis”
Providing hypothesis examples
Giving you tips for how to write your own hypothesis

So let’s get started!

What Is a Hypothesis?

Merriam Webster defines a hypothesis as “an assumption or concession made for the sake of argument.” In other words, a hypothesis is an educated guess . Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it’s true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an experiment that tests your hypothesis in order for it to be valid.

As you could assume from that statement, it’s easy to make a bad hypothesis. But when you’re holding an experiment, it’s even more important that your guesses be good...after all, you’re spending time (and maybe money!) to figure out more about your observation. That’s why we refer to a hypothesis as an educated guess--good hypotheses are based on existing data and research to make them as sound as possible.

Hypotheses are one part of what’s called the scientific method . Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. It’s important that you understand the concepts of the scientific method before holding your own experiment. Though it may vary among scientists, the scientific method is generally made up of six steps (in order):

Observation
Asking questions
Forming a hypothesis
Analyze the data
Communicate your results

You’ll notice that the hypothesis comes pretty early on when conducting an experiment. That’s because experiments work best when they’re trying to answer one specific question. And you can’t conduct an experiment until you know what you’re trying to prove!

Independent and Dependent Variables

After doing your research, you’re ready for another important step in forming your hypothesis: identifying variables. Variables are basically any factor that could influence the outcome of your experiment . Variables have to be measurable and related to the topic being studied.

There are two types of variables: independent variables and dependent variables. I ndependent variables remain constant . For example, age is an independent variable; it will stay the same, and researchers can look at different ages to see if it has an effect on the dependent variable.

Speaking of dependent variables... dependent variables are subject to the influence of the independent variable , meaning that they are not constant. Let’s say you want to test whether a person’s age affects how much sleep they need. In that case, the independent variable is age (like we mentioned above), and the dependent variable is how much sleep a person gets.

Variables will be crucial in writing your hypothesis. You need to be able to identify which variable is which, as both the independent and dependent variables will be written into your hypothesis. For instance, in a study about exercise, the independent variable might be the speed at which the respondents walk for thirty minutes, and the dependent variable would be their heart rate. In your study and in your hypothesis, you’re trying to understand the relationship between the two variables.

Elements of a Good Hypothesis

The best hypotheses start by asking the right questions . For instance, if you’ve observed that the grass is greener when it rains twice a week, you could ask what kind of grass it is, what elevation it’s at, and if the grass across the street responds to rain in the same way. Any of these questions could become the backbone of experiments to test why the grass gets greener when it rains fairly frequently.

As you’re asking more questions about your first observation, make sure you’re also making more observations . If it doesn’t rain for two weeks and the grass still looks green, that’s an important observation that could influence your hypothesis. You'll continue observing all throughout your experiment, but until the hypothesis is finalized, every observation should be noted.

Finally, you should consult secondary research before writing your hypothesis . Secondary research is comprised of results found and published by other people. You can usually find this information online or at your library. Additionally, m ake sure the research you find is credible and related to your topic. If you’re studying the correlation between rain and grass growth, it would help you to research rain patterns over the past twenty years for your county, published by a local agricultural association. You should also research the types of grass common in your area, the type of grass in your lawn, and whether anyone else has conducted experiments about your hypothesis. Also be sure you’re checking the quality of your research . Research done by a middle school student about what minerals can be found in rainwater would be less useful than an article published by a local university.

Writing Your Hypothesis

Once you’ve considered all of the factors above, you’re ready to start writing your hypothesis. Hypotheses usually take a certain form when they’re written out in a research report.

When you boil down your hypothesis statement, you are writing down your best guess and not the question at hand . This means that your statement should be written as if it is fact already, even though you are simply testing it.

The reason for this is that, after you have completed your study, you'll either accept or reject your if-then or your null hypothesis. All hypothesis testing examples should be measurable and able to be confirmed or denied. You cannot confirm a question, only a statement!

In fact, you come up with hypothesis examples all the time! For instance, when you guess on the outcome of a basketball game, you don’t say, “Will the Miami Heat beat the Boston Celtics?” but instead, “I think the Miami Heat will beat the Boston Celtics.” You state it as if it is already true, even if it turns out you’re wrong. You do the same thing when writing your hypothesis.

Additionally, keep in mind that hypotheses can range from very specific to very broad. These hypotheses can be specific, but if your hypothesis testing examples involve a broad range of causes and effects, your hypothesis can also be broad.

The Two Types of Hypotheses

Now that you understand what goes into a hypothesis, it’s time to look more closely at the two most common types of hypothesis: the if-then hypothesis and the null hypothesis.

#1: If-Then Hypotheses

First of all, if-then hypotheses typically follow this formula:

If ____ happens, then ____ will happen.

The goal of this type of hypothesis is to test the causal relationship between the independent and dependent variable. It’s fairly simple, and each hypothesis can vary in how detailed it can be. We create if-then hypotheses all the time with our daily predictions. Here are some examples of hypotheses that use an if-then structure from daily life:

If I get enough sleep, I’ll be able to get more work done tomorrow.
If the bus is on time, I can make it to my friend’s birthday party.
If I study every night this week, I’ll get a better grade on my exam.

In each of these situations, you’re making a guess on how an independent variable (sleep, time, or studying) will affect a dependent variable (the amount of work you can do, making it to a party on time, or getting better grades).

You may still be asking, “What is an example of a hypothesis used in scientific research?” Take one of the hypothesis examples from a real-world study on whether using technology before bed affects children’s sleep patterns. The hypothesis read s:

“We hypothesized that increased hours of tablet- and phone-based screen time at bedtime would be inversely correlated with sleep quality and child attention.”

It might not look like it, but this is an if-then statement. The researchers basically said, “If children have more screen usage at bedtime, then their quality of sleep and attention will be worse.” The sleep quality and attention are the dependent variables and the screen usage is the independent variable. (Usually, the independent variable comes after the “if” and the dependent variable comes after the “then,” as it is the independent variable that affects the dependent variable.) This is an excellent example of how flexible hypothesis statements can be, as long as the general idea of “if-then” and the independent and dependent variables are present.

#2: Null Hypotheses

Your if-then hypothesis is not the only one needed to complete a successful experiment, however. You also need a null hypothesis to test it against. In its most basic form, the null hypothesis is the opposite of your if-then hypothesis . When you write your null hypothesis, you are writing a hypothesis that suggests that your guess is not true, and that the independent and dependent variables have no relationship .

One null hypothesis for the cell phone and sleep study from the last section might say:

“If children have more screen usage at bedtime, their quality of sleep and attention will not be worse.”

In this case, this is a null hypothesis because it’s asking the opposite of the original thesis!

Conversely, if your if-then hypothesis suggests that your two variables have no relationship, then your null hypothesis would suggest that there is one. So, pretend that there is a study that is asking the question, “Does the amount of followers on Instagram influence how long people spend on the app?” The independent variable is the amount of followers, and the dependent variable is the time spent. But if you, as the researcher, don’t think there is a relationship between the number of followers and time spent, you might write an if-then hypothesis that reads:

“If people have many followers on Instagram, they will not spend more time on the app than people who have less.”

In this case, the if-then suggests there isn’t a relationship between the variables. In that case, one of the null hypothesis examples might say:

“If people have many followers on Instagram, they will spend more time on the app than people who have less.”

You then test both the if-then and the null hypothesis to gauge if there is a relationship between the variables, and if so, how much of a relationship.

4 Tips to Write the Best Hypothesis

If you’re going to take the time to hold an experiment, whether in school or by yourself, you’re also going to want to take the time to make sure your hypothesis is a good one. The best hypotheses have four major elements in common: plausibility, defined concepts, observability, and general explanation.

#1: Plausibility

At first glance, this quality of a hypothesis might seem obvious. When your hypothesis is plausible, that means it’s possible given what we know about science and general common sense. However, improbable hypotheses are more common than you might think.

Imagine you’re studying weight gain and television watching habits. If you hypothesize that people who watch more than twenty hours of television a week will gain two hundred pounds or more over the course of a year, this might be improbable (though it’s potentially possible). Consequently, c ommon sense can tell us the results of the study before the study even begins.

Improbable hypotheses generally go against science, as well. Take this hypothesis example:

“If a person smokes one cigarette a day, then they will have lungs just as healthy as the average person’s.”

This hypothesis is obviously untrue, as studies have shown again and again that cigarettes negatively affect lung health. You must be careful that your hypotheses do not reflect your own personal opinion more than they do scientifically-supported findings. This plausibility points to the necessity of research before the hypothesis is written to make sure that your hypothesis has not already been disproven.

#2: Defined Concepts

The more advanced you are in your studies, the more likely that the terms you’re using in your hypothesis are specific to a limited set of knowledge. One of the hypothesis testing examples might include the readability of printed text in newspapers, where you might use words like “kerning” and “x-height.” Unless your readers have a background in graphic design, it’s likely that they won’t know what you mean by these terms. Thus, it’s important to either write what they mean in the hypothesis itself or in the report before the hypothesis.

Here’s what we mean. Which of the following sentences makes more sense to the common person?

If the kerning is greater than average, more words will be read per minute.

If the space between letters is greater than average, more words will be read per minute.

For people reading your report that are not experts in typography, simply adding a few more words will be helpful in clarifying exactly what the experiment is all about. It’s always a good idea to make your research and findings as accessible as possible.

Good hypotheses ensure that you can observe the results.

#3: Observability

In order to measure the truth or falsity of your hypothesis, you must be able to see your variables and the way they interact. For instance, if your hypothesis is that the flight patterns of satellites affect the strength of certain television signals, yet you don’t have a telescope to view the satellites or a television to monitor the signal strength, you cannot properly observe your hypothesis and thus cannot continue your study.

Some variables may seem easy to observe, but if you do not have a system of measurement in place, you cannot observe your hypothesis properly. Here’s an example: if you’re experimenting on the effect of healthy food on overall happiness, but you don’t have a way to monitor and measure what “overall happiness” means, your results will not reflect the truth. Monitoring how often someone smiles for a whole day is not reasonably observable, but having the participants state how happy they feel on a scale of one to ten is more observable.

In writing your hypothesis, always keep in mind how you'll execute the experiment.

#4: Generalizability

Perhaps you’d like to study what color your best friend wears the most often by observing and documenting the colors she wears each day of the week. This might be fun information for her and you to know, but beyond you two, there aren’t many people who could benefit from this experiment. When you start an experiment, you should note how generalizable your findings may be if they are confirmed. Generalizability is basically how common a particular phenomenon is to other people’s everyday life.

Let’s say you’re asking a question about the health benefits of eating an apple for one day only, you need to realize that the experiment may be too specific to be helpful. It does not help to explain a phenomenon that many people experience. If you find yourself with too specific of a hypothesis, go back to asking the big question: what is it that you want to know, and what do you think will happen between your two variables?

Hypothesis Testing Examples

We know it can be hard to write a good hypothesis unless you’ve seen some good hypothesis examples. We’ve included four hypothesis examples based on some made-up experiments. Use these as templates or launch pads for coming up with your own hypotheses.

Experiment #1: Students Studying Outside (Writing a Hypothesis)

You are a student at PrepScholar University. When you walk around campus, you notice that, when the temperature is above 60 degrees, more students study in the quad. You want to know when your fellow students are more likely to study outside. With this information, how do you make the best hypothesis possible?

You must remember to make additional observations and do secondary research before writing your hypothesis. In doing so, you notice that no one studies outside when it’s 75 degrees and raining, so this should be included in your experiment. Also, studies done on the topic beforehand suggested that students are more likely to study in temperatures less than 85 degrees. With this in mind, you feel confident that you can identify your variables and write your hypotheses:

If-then: “If the temperature in Fahrenheit is less than 60 degrees, significantly fewer students will study outside.”

Null: “If the temperature in Fahrenheit is less than 60 degrees, the same number of students will study outside as when it is more than 60 degrees.”

These hypotheses are plausible, as the temperatures are reasonably within the bounds of what is possible. The number of people in the quad is also easily observable. It is also not a phenomenon specific to only one person or at one time, but instead can explain a phenomenon for a broader group of people.

To complete this experiment, you pick the month of October to observe the quad. Every day (except on the days where it’s raining)from 3 to 4 PM, when most classes have released for the day, you observe how many people are on the quad. You measure how many people come and how many leave. You also write down the temperature on the hour.

After writing down all of your observations and putting them on a graph, you find that the most students study on the quad when it is 70 degrees outside, and that the number of students drops a lot once the temperature reaches 60 degrees or below. In this case, your research report would state that you accept or “failed to reject” your first hypothesis with your findings.

Experiment #2: The Cupcake Store (Forming a Simple Experiment)

Let’s say that you work at a bakery. You specialize in cupcakes, and you make only two colors of frosting: yellow and purple. You want to know what kind of customers are more likely to buy what kind of cupcake, so you set up an experiment. Your independent variable is the customer’s gender, and the dependent variable is the color of the frosting. What is an example of a hypothesis that might answer the question of this study?

Here’s what your hypotheses might look like:

If-then: “If customers’ gender is female, then they will buy more yellow cupcakes than purple cupcakes.”

Null: “If customers’ gender is female, then they will be just as likely to buy purple cupcakes as yellow cupcakes.”

This is a pretty simple experiment! It passes the test of plausibility (there could easily be a difference), defined concepts (there’s nothing complicated about cupcakes!), observability (both color and gender can be easily observed), and general explanation ( this would potentially help you make better business decisions ).

Experiment #3: Backyard Bird Feeders (Integrating Multiple Variables and Rejecting the If-Then Hypothesis)

While watching your backyard bird feeder, you realized that different birds come on the days when you change the types of seeds. You decide that you want to see more cardinals in your backyard, so you decide to see what type of food they like the best and set up an experiment.

However, one morning, you notice that, while some cardinals are present, blue jays are eating out of your backyard feeder filled with millet. You decide that, of all of the other birds, you would like to see the blue jays the least. This means you'll have more than one variable in your hypothesis. Your new hypotheses might look like this:

If-then: “If sunflower seeds are placed in the bird feeders, then more cardinals will come than blue jays. If millet is placed in the bird feeders, then more blue jays will come than cardinals.”

Null: “If either sunflower seeds or millet are placed in the bird, equal numbers of cardinals and blue jays will come.”

Through simple observation, you actually find that cardinals come as often as blue jays when sunflower seeds or millet is in the bird feeder. In this case, you would reject your “if-then” hypothesis and “fail to reject” your null hypothesis . You cannot accept your first hypothesis, because it’s clearly not true. Instead you found that there was actually no relation between your different variables. Consequently, you would need to run more experiments with different variables to see if the new variables impact the results.

Experiment #4: In-Class Survey (Including an Alternative Hypothesis)

You’re about to give a speech in one of your classes about the importance of paying attention. You want to take this opportunity to test a hypothesis you’ve had for a while:

If-then: If students sit in the first two rows of the classroom, then they will listen better than students who do not.

Null: If students sit in the first two rows of the classroom, then they will not listen better or worse than students who do not.

You give your speech and then ask your teacher if you can hand out a short survey to the class. On the survey, you’ve included questions about some of the topics you talked about. When you get back the results, you’re surprised to see that not only do the students in the first two rows not pay better attention, but they also scored worse than students in other parts of the classroom! Here, both your if-then and your null hypotheses are not representative of your findings. What do you do?

This is when you reject both your if-then and null hypotheses and instead create an alternative hypothesis . This type of hypothesis is used in the rare circumstance that neither of your hypotheses is able to capture your findings . Now you can use what you’ve learned to draft new hypotheses and test again!

Key Takeaways: Hypothesis Writing

The more comfortable you become with writing hypotheses, the better they will become. The structure of hypotheses is flexible and may need to be changed depending on what topic you are studying. The most important thing to remember is the purpose of your hypothesis and the difference between the if-then and the null . From there, in forming your hypothesis, you should constantly be asking questions, making observations, doing secondary research, and considering your variables. After you have written your hypothesis, be sure to edit it so that it is plausible, clearly defined, observable, and helpful in explaining a general phenomenon.

Writing a hypothesis is something that everyone, from elementary school children competing in a science fair to professional scientists in a lab, needs to know how to do. Hypotheses are vital in experiments and in properly executing the scientific method . When done correctly, hypotheses will set up your studies for success and help you to understand the world a little better, one experiment at a time.

What’s Next?

If you’re studying for the science portion of the ACT, there’s definitely a lot you need to know. We’ve got the tools to help, though! Start by checking out our ultimate study guide for the ACT Science subject test. Once you read through that, be sure to download our recommended ACT Science practice tests , since they’re one of the most foolproof ways to improve your score. (And don’t forget to check out our expert guide book , too.)

If you love science and want to major in a scientific field, you should start preparing in high school . Here are the science classes you should take to set yourself up for success.

If you’re trying to think of science experiments you can do for class (or for a science fair!), here’s a list of 37 awesome science experiments you can do at home

Ashley Sufflé Robinson has a Ph.D. in 19th Century English Literature. As a content writer for PrepScholar, Ashley is passionate about giving college-bound students the in-depth information they need to get into the school of their dreams.

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How to Write a Hypothesis? Types and Examples

All research studies involve the use of the scientific method, which is a mathematical and experimental technique used to conduct experiments by developing and testing a hypothesis or a prediction about an outcome. Simply put, a hypothesis is a suggested solution to a problem. It includes elements that are expressed in terms of relationships with each other to explain a condition or an assumption that hasn’t been verified using facts. 1 The typical steps in a scientific method include developing such a hypothesis, testing it through various methods, and then modifying it based on the outcomes of the experiments.

A research hypothesis can be defined as a specific, testable prediction about the anticipated results of a study. 2 Hypotheses help guide the research process and supplement the aim of the study. After several rounds of testing, hypotheses can help develop scientific theories. 3 Hypotheses are often written as if-then statements.

Here are two hypothesis examples:

Dandelions growing in nitrogen-rich soils for two weeks develop larger leaves than those in nitrogen-poor soils because nitrogen stimulates vegetative growth. 4

If a company offers flexible work hours, then their employees will be happier at work. 5

What is a hypothesis?
Types of hypotheses
Characteristics of a hypothesis
Functions of a hypothesis
How to write a hypothesis
Hypothesis examples
Frequently asked questions

What is a hypothesis?

A hypothesis expresses an expected relationship between variables in a study and is developed before conducting any research. Hypotheses are not opinions but rather are expected relationships based on facts and observations. They help support scientific research and expand existing knowledge. An incorrectly formulated hypothesis can affect the entire experiment leading to errors in the results so it’s important to know how to formulate a hypothesis and develop it carefully.

A few sources of a hypothesis include observations from prior studies, current research and experiences, competitors, scientific theories, and general conditions that can influence people. Figure 1 depicts the different steps in a research design and shows where exactly in the process a hypothesis is developed. 4

There are seven different types of hypotheses—simple, complex, directional, nondirectional, associative and causal, null, and alternative.

Types of hypotheses

The seven types of hypotheses are listed below: 5 , 6,7

Simple : Predicts the relationship between a single dependent variable and a single independent variable.

Example: Exercising in the morning every day will increase your productivity.

Complex : Predicts the relationship between two or more variables.

Example: Spending three hours or more on social media daily will negatively affect children’s mental health and productivity, more than that of adults.

Directional : Specifies the expected direction to be followed and uses terms like increase, decrease, positive, negative, more, or less.

Example: The inclusion of intervention X decreases infant mortality compared to the original treatment.

Non-directional : Does not predict the exact direction, nature, or magnitude of the relationship between two variables but rather states the existence of a relationship. This hypothesis may be used when there is no underlying theory or if findings contradict prior research.

Example: Cats and dogs differ in the amount of affection they express.

Associative and causal : An associative hypothesis suggests an interdependency between variables, that is, how a change in one variable changes the other.

Example: There is a positive association between physical activity levels and overall health.

A causal hypothesis, on the other hand, expresses a cause-and-effect association between variables.

Example: Long-term alcohol use causes liver damage.

Null : Claims that the original hypothesis is false by showing that there is no relationship between the variables.

Example: Sleep duration does not have any effect on productivity.

Alternative : States the opposite of the null hypothesis, that is, a relationship exists between two variables.

Example: Sleep duration affects productivity.

Characteristics of a hypothesis

So, what makes a good hypothesis? Here are some important characteristics of a hypothesis. 8,9

Testable : You must be able to test the hypothesis using scientific methods to either accept or reject the prediction.
Falsifiable : It should be possible to collect data that reject rather than support the hypothesis.
Logical : Hypotheses shouldn’t be a random guess but rather should be based on previous theories, observations, prior research, and logical reasoning.
Positive : The hypothesis statement about the existence of an association should be positive, that is, it should not suggest that an association does not exist. Therefore, the language used and knowing how to phrase a hypothesis is very important.
Clear and accurate : The language used should be easily comprehensible and use correct terminology.
Relevant : The hypothesis should be relevant and specific to the research question.
Structure : Should include all the elements that make a good hypothesis: variables, relationship, and outcome.

Functions of a hypothesis

The following list mentions some important functions of a hypothesis: 1

Maintains the direction and progress of the research.
Expresses the important assumptions underlying the proposition in a single statement.
Establishes a suitable context for researchers to begin their investigation and for readers who are referring to the final report.
Provides an explanation for the occurrence of a specific phenomenon.
Ensures selection of appropriate and accurate facts necessary and relevant to the research subject.

To summarize, a hypothesis provides the conceptual elements that complete the known data, conceptual relationships that systematize unordered elements, and conceptual meanings and interpretations that explain the unknown phenomena. 1

How to write a hypothesis

Listed below are the main steps explaining how to write a hypothesis. 2,4,5

Make an observation and identify variables : Observe the subject in question and try to recognize a pattern or a relationship between the variables involved. This step provides essential background information to begin your research.

For example, if you notice that an office’s vending machine frequently runs out of a specific snack, you may predict that more people in the office choose that snack over another.

Identify the main research question : After identifying a subject and recognizing a pattern, the next step is to ask a question that your hypothesis will answer.

For example, after observing employees’ break times at work, you could ask “why do more employees take breaks in the morning rather than in the afternoon?”

Conduct some preliminary research to ensure originality and novelty : Your initial answer, which is your hypothesis, to the question is based on some pre-existing information about the subject. However, to ensure that your hypothesis has not been asked before or that it has been asked but rejected by other researchers you would need to gather additional information.

For example, based on your observations you might state a hypothesis that employees work more efficiently when the air conditioning in the office is set at a lower temperature. However, during your preliminary research you find that this hypothesis was proven incorrect by a prior study.

Develop a general statement : After your preliminary research has confirmed the originality of your proposed answer, draft a general statement that includes all variables, subjects, and predicted outcome. The statement could be if/then or declarative.
Finalize the hypothesis statement : Use the PICOT model, which clarifies how to word a hypothesis effectively, when finalizing the statement. This model lists the important components required to write a hypothesis.

P opulation: The specific group or individual who is the main subject of the research

I nterest: The main concern of the study/research question

C omparison: The main alternative group

O utcome: The expected results

T ime: Duration of the experiment

Once you’ve finalized your hypothesis statement you would need to conduct experiments to test whether the hypothesis is true or false.

Hypothesis examples

The following table provides examples of different types of hypotheses. 10 ,11


Null	Hyperactivity is not related to eating sugar.
There is no relationship between height and shoe size.
Alternative	Hyperactivity is positively related to eating sugar.
There is a positive association between height and shoe size.
Simple	Students who eat breakfast perform better in exams than students who don’t eat breakfast.
Reduced screen time improves sleep quality.
Complex	People with high-sugar diet and sedentary activity levels are more likely to develop depression.
Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone.
Directional	As job satisfaction increases, the rate of employee turnover decreases.
Increase in sun exposure increases the risk of skin cancer.
Non-directional	College students will perform differently from elementary school students on a memory task.
Advertising exposure correlates with variations in purchase decisions among consumers.
Associative	Hospitals have more sick people in them than other institutions in society.
Watching TV is related to increased snacking.
Causal	Inadequate sleep decreases memory retention.
Recreational drugs cause psychosis.

Key takeaways

Here’s a summary of all the key points discussed in this article about how to write a hypothesis.

A hypothesis is an assumption about an association between variables made based on limited evidence, which should be tested.
A hypothesis has four parts—the research question, independent variable, dependent variable, and the proposed relationship between the variables.
The statement should be clear, concise, testable, logical, and falsifiable.
There are seven types of hypotheses—simple, complex, directional, non-directional, associative and causal, null, and alternative.
A hypothesis provides a focus and direction for the research to progress.
A hypothesis plays an important role in the scientific method by helping to create an appropriate experimental design.

Frequently asked questions

Hypotheses and research questions have different objectives and structure. The following table lists some major differences between the two. 9


Includes a prediction based on the proposed research	No prediction is made
Designed to forecast the relationship of and between two or more variables	Variables may be explored
Closed ended	Open ended, invites discussion
Used if the research topic is well established and there is certainty about the relationship between the variables	Used for new topics that haven’t been researched extensively. The relationship between different variables is less known

Here are a few examples to differentiate between a research question and hypothesis.


What is the effect of eating an apple a day by adults aged over 60 years on the frequency of physician visits?	Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits
What is the effect of flexible or fixed working hours on employee job satisfaction?	Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.
Does drinking coffee in the morning affect employees’ productivity?	Drinking coffee in the morning improves employees’ productivity.

Yes, here’s a simple checklist to help you gauge the effectiveness of your hypothesis. 9 1. When writing a hypothesis statement, check if it: 2. Predicts the relationship between the stated variables and the expected outcome. 3. Uses simple and concise language and is not wordy. 4. Does not assume readers’ knowledge about the subject. 5. Has observable, falsifiable, and testable results.

As mentioned earlier in this article, a hypothesis is an assumption or prediction about an association between variables based on observations and simple evidence. These statements are usually generic. Research objectives, on the other hand, are more specific and dictated by hypotheses. The same hypothesis can be tested using different methods and the research objectives could be different in each case. For example, Louis Pasteur observed that food lasts longer at higher altitudes, reasoned that it could be because the air at higher altitudes is cleaner (with fewer or no germs), and tested the hypothesis by exposing food to air cleaned in the laboratory. 12 Thus, a hypothesis is predictive—if the reasoning is correct, X will lead to Y—and research objectives are developed to test these predictions.

Null hypothesis testing is a method to decide between two assumptions or predictions between variables (null and alternative hypotheses) in a statistical relationship in a sample. The null hypothesis, denoted as H 0 , claims that no relationship exists between variables in a population and any relationship in the sample reflects a sampling error or occurrence by chance. The alternative hypothesis, denoted as H 1 , claims that there is a relationship in the population. In every study, researchers need to decide whether the relationship in a sample occurred by chance or reflects a relationship in the population. This is done by hypothesis testing using the following steps: 13 1. Assume that the null hypothesis is true. 2. Determine how likely the sample relationship would be if the null hypothesis were true. This probability is called the p value. 3. If the sample relationship would be extremely unlikely, reject the null hypothesis and accept the alternative hypothesis. If the relationship would not be unlikely, accept the null hypothesis.

To summarize, researchers should know how to write a good hypothesis to ensure that their research progresses in the required direction. A hypothesis is a testable prediction about any behavior or relationship between variables, usually based on facts and observation, and states an expected outcome.

We hope this article has provided you with essential insight into the different types of hypotheses and their functions so that you can use them appropriately in your next research project.

References

Dalen, DVV. The function of hypotheses in research. Proquest website. Accessed April 8, 2024. https://www.proquest.com/docview/1437933010?pq-origsite=gscholar&fromopenview=true&sourcetype=Scholarly%20Journals&imgSeq=1
McLeod S. Research hypothesis in psychology: Types & examples. SimplyPsychology website. Updated December 13, 2023. Accessed April 9, 2024. https://www.simplypsychology.org/what-is-a-hypotheses.html
Scientific method. Britannica website. Updated March 14, 2024. Accessed April 9, 2024. https://www.britannica.com/science/scientific-method
The hypothesis in science writing. Accessed April 10, 2024. https://berks.psu.edu/sites/berks/files/campus/HypothesisHandout_Final.pdf
How to develop a hypothesis (with elements, types, and examples). Indeed.com website. Updated February 3, 2023. Accessed April 10, 2024. https://www.indeed.com/career-advice/career-development/how-to-write-a-hypothesis
Types of research hypotheses. Excelsior online writing lab. Accessed April 11, 2024. https://owl.excelsior.edu/research/research-hypotheses/types-of-research-hypotheses/
What is a research hypothesis: how to write it, types, and examples. Researcher.life website. Published February 8, 2023. Accessed April 11, 2024. https://researcher.life/blog/article/how-to-write-a-research-hypothesis-definition-types-examples/
Developing a hypothesis. Pressbooks website. Accessed April 12, 2024. https://opentext.wsu.edu/carriecuttler/chapter/developing-a-hypothesis/
What is and how to write a good hypothesis in research. Elsevier author services website. Accessed April 12, 2024. https://scientific-publishing.webshop.elsevier.com/manuscript-preparation/what-how-write-good-hypothesis-research/
How to write a great hypothesis. Verywellmind website. Updated March 12, 2023. Accessed April 13, 2024. https://www.verywellmind.com/what-is-a-hypothesis-2795239
15 Hypothesis examples. Helpfulprofessor.com Published September 8, 2023. Accessed March 14, 2024. https://helpfulprofessor.com/hypothesis-examples/
Editage insights. What is the interconnectivity between research objectives and hypothesis? Published February 24, 2021. Accessed April 13, 2024. https://www.editage.com/insights/what-is-the-interconnectivity-between-research-objectives-and-hypothesis
Understanding null hypothesis testing. BCCampus open publishing. Accessed April 16, 2024. https://opentextbc.ca/researchmethods/chapter/understanding-null-hypothesis-testing/#:~:text=In%20null%20hypothesis%20testing%2C%20this,said%20to%20be%20statistically%20significant

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National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
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experiments disproving spontaneous generation

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

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Developing a Hypothesis

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.

Theories and hypotheses always have this if-then relationship. “ If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.3 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274 ↵
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Developing a Hypothesis Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Suppose I’m going on a picnic and I’m only selecting items that fit a certain rule. You want to find out what rule I’m using, so you offer up some guesses at items I might want to bring:

An Egg Salad Sandwich

A grape soda

Suppose now that I tell you that I’m okay with the first two, but I won’t bring the third. Your next step is interesting: you look at the first two, figure out what they have in common, and then you take a guess at the rule I’m using. In other words, you posit a hypothesis. You say something like

Do you only want to bring things that are yellow or tan?

Notice how at this point your hypothesis goes way beyond the evidence. Bananas and egg salad sandwiches have so much more in common than being yellow/tan objects. This is how hypothetical reasoning works: you look at the evidence, add a hypothesis that makes sense of that evidence (one among many hypotheses available), and then check to be sure that your hypothesis continues to make sense of new evidence as it is collected.

Suppose I now tell you that you haven’t guessed the right rule. So, you might throw out some more objects:

A key lime pie

A jug of orange juice

I then tell you that the first two are okay, but again the last item is not going with me on this picnic.

It’s solid items! Solid items are okay, but liquid items are not.

Again, not quite. Try another set of items. You are still convinced that it has to do with the soda and the juice being liquid, so you try out an interesting tactic:

An ice cube

Some liquid water

Some water Vapor

The first and last items are okay, but not the middle one. Now you think you’ve got me. You guess that the rule is “anything but liquids,” but I refuse to tell you whether you got it right. You’re pretty confident at this point, but perhaps you’re not certain . In principle, there could always be more evidence that upsets your hypothesis. I might say that the ocean is okay but a fresh water lake isn’t, and that would be very confusing for you. You’ll never be quite certain that you’ve guessed my rule correctly because it’s always in principle possible that I have a super complex rule that is more complex than your hypothesis.

So in hypothetical reasoning what we’re doing is making a leap from the evidence we have available to the rule or principle or theory which explains that evidence. The hypothesis is the link between the two. We have some finite evidence available to us, and we hypothesize an explanation. The explanation we posit either is or is not the true explanation, and so we’re using the hypothesis as a bridge to get onto the true explanation of what is happening in the world.

The hypothetical method has four stages. Let’s illustrate each with an example. You are investigating a murder and have collected a lot of evidence but do not yet have a guess as to who the killer might be.

1. The occurrence of a problem

Example $\PageIndex{1}$

Someone has been murdered and we need to find out who the killer is so that we might bring them to justice.

2. Formulating a hypothesis

Example $\PageIndex{2}$

After collecting some evidence, you weigh the reasons in favor of thinking that each suspect is indeed the murderer, and you decide that the spouse is responsible.

3. Drawing implications from the hypothesis

Example $\PageIndex{3}$

If the spouse was the murderer, then a number of things follow. The spouse must have a weak alibi or their alibi must rest on some falsehood. There is likely to be some evidence on their property or among their belongings that links the spouse to the murder. The spouse likely had motive. etc., etc., etc.

We can go on for ages, but the basic point is that once we’ve got an idea of what the explanation for the murder is (in this case, the hypothesis is that the spouse murdered the victim), we can ask ourselves what the world would have to be like for that to have been true. Then we move onto the final step:

4. Test those implications.

Example $\PageIndex{4}$

We can search the murder scene, try to find a murder weapon, run DNA analysis on the organic matter left at the scene, question the spouse about their alibi and possible motives, check their bank accounts, talk to friends and neighbors, etc. Once we have a hypothesis, in other words, that hypothesis drives the search for new evidence—it tells us what might be relevant and what irrelevant and therefore what is worth our time and what is not.

The Logic of Hypothetical Reasoning

If the spouse did it, then they must have a weak alibi. Their alibi is only verifiable by one person: the victim. So they do have a weak alibi. Therefore...they did it? Not quite.

Just because they have a weak alibi doesn’t mean they did it. If that were true, anyone with a weak alibi would be guilty for everything bad that happened when they weren’t busy with a verifiable activity.

Similarly, if your car’s battery is dead, then it won’t start. This doesn’t mean that whenever your car doesn’t start, the battery is dead. That would be a wild and bananas claim to make (and obviously false), but the original conditional (the first sentence in this paragraph) isn’t wild and bananas. In fact, it’s a pretty normal claim to make and it seems obviously true.

Let’s talk briefly about the logic of hypothetical reasoning so we can discover an important truth.

If the spouse did it, then their alibi will be weak

Their alibi is weak

So, the spouse did it

This is bad reasoning. How do we know? Well, here’s the logical form:

If A, then B

Therefore, A

This argument structure—called “affirming the consequent”—is invalid because there are countless instances of this general structure that have true premises and a false conclusion. Consider the following examples:

Example $\PageIndex{5}$

If I cook, I eat well

I ate well tonight, so I cooked.

Example $\PageIndex{6}$

If Eric runs for student president, he’ll become more popular.

Eric did become more popular, so he must’ve run for student president.

Maybe I ate well because I’m at the finest restaurant in town. Maybe I ate well because my brother cooked for me. Any of these things is possible, which is the root problem with this argument structure. It infers that one of the many possible antecedents to the conditional is the true antecedent without giving any reason for choosing or preferring this antecedent.

More concretely, affirming the consequent is the structure of an argument that states that a) one thing will explain an event, and b) that the event in question in fact occurred, and then concludes that c) the one thing that would’ve explained the event is the correct explanation of the event.

More concretely still, here’s yet another example of affirming the consequent:

Example $\PageIndex{7}$

My being rich would explain my being popular

I am in fact popular,

Therefore I am in fact rich

I might be popular without having a penny to my name. People sometimes root for underdogs, or respond to the right kind of personality regardless of their socioeconomic standing, or respect a good sense of humor or athletic prowess.

If I were rich, though, that would be one potential explanation for my being popular. Rich people have nice clothes, cool cars, nice houses, and get to have the kinds of experiences that make someone a potentially popular person because everyone wants to hear the cool stories or be associated with the exciting life they lead. Perhaps, people often seem to think, they’ll get to participate in the next adventure if they cozy up to the rich people. Rich kids in high school can also throw the best parties (if we’re honest, and that’s a great source of popularity).

But If I’m not rich, that doesn’t mean I’m not popular. It only means that I’m not popular because I’m rich .

Okay, so we’ve established that hypothetical reasoning has the logical structure of affirming the consequent. We’ve further established that affirming the consequent is an invalid deductive argumentative structure. Where does this leave us? Is the hypothetical method bad reasoning ?!?!?!? Nope! Luckily not all reasoning is deductive reasoning.

Remember that we’re discussing inductive reasoning in this chapter. Inductive reasoning doesn’t obey the rules of deductive logic. So it’s no crime for a method of inductive reasoning to be deductively invalid. The crime against logic would be to claim that we have certain knowledge when we only use inductive reasoning to justify that knowledge. The upshot? Science doesn’t produce certain knowledge—it produces justified knowledge, knowledge to a more or less high degree of certitude, knowledge that we can rely on and build bridges on, knowledge that almost certainly won’t let us down (but it doesn’t produce certain knowledge).

We can, though, with deductive certainty, falsify a hypothesis. Consider the murder case: if the spouse did it, then they’d have a weak alibi. That is, if the spouse did it, then they wouldn’t have an airtight alibi because they’d have to be lying about where they were when the murder took place. If it turns out that the spouse does have an airtight alibi, then your hypothesis was wrong.

Let’s take a look at the logic of falsification:

If the spouse did it, then they won’t have an airtight alibi

They have an airtight alibi

So the spouse didn’t do it

Now it’s possible that the conditional premise (the first premise) isn’t true, but we’ll assume it’s true for the sake of the illustration. The hypothesis was that the spouse did it and so the spouse’s alibi must have some weakness.

It’s also possible that our detective work hasn’t been thorough enough and so the second premise is false. These are important possibilities to keep in mind. Either way, here’s the logical form (a bit cleaned up and simplified):

Therefore not A

This is what argument pattern? That’s right! You’re so smart! It’s modus tollens or “the method of denying”. It’s a type of argument where you deny the implications of something and thereby deny that very thing. It’s a deductively valid argument form (remember from our unit on natural deduction?), so we can falsify hypotheses with deductive certainty: if your hypothesis implies something with necessity, and that something doesn’t come to pass, then your hypothesis is wrong.

Your hypothesis is wrong. That is, your hypothesis as it stands was wrong. You might be like one of those rogue and dogged detectives in the television shows that never gives up on a hunch and ultimately discovers the truth through sheer stubbornness and determination. You might think that the spouse did it, even though they’ve got an airtight alibi. In that case, you’ll have to alter your hypothesis a bit.

The process of altering a hypothesis to react to potentially falsifying evidence typically involves adding extra hypotheses onto your original hypothesis such that the original hypothesis no longer has the troubling implications which turned out not to be true. These extra hypotheses are called ad hoc hypotheses.

As an example, Newton’s theory of gravity had one problem: it made a sort of wacky prediction. So the idea was that gravity was an instantaneous attractive force exerted by all massive bodies on all other bodies. That is, all bodies attract all other bodies regardless of distance or time. The result of this should be that all massive bodies should smack into each other over time (after all, they still have to travel towards one another). But we don’t witness this. We should see things crashing towards the center of gravity of the universe at incredible speeds, but that’s not what’s happening. So, by the logic of falsification, Newton’s theory is simply false.

But Newton had a trick up his sleeve: he claimed that God arranged things such that the heavenly bodies are so far apart from one another that they are prevented from crashing into one another. Problem solved! God put things in the right spatial orientation such that the theory of gravity is saved: they won’t crash into each other because they’re so far apart! Newton employed an ad hoc hypothesis to save his theory from falsification.

Abductive Reasoning

There’s one more thing to discuss while we’re still on the topic of hypothetical reasoning or reasoning using hypotheses. ‘Abduction’ is a fancy word for a process or method sometimes called “inference to the best explanation. The basic idea is that we have a bunch of evidence, we try to explain it, and we find that we could explain it in multiple ways. Then we find the “best” explanation or hypothesis and infer that this is the true explanation.

For example, say we’re playing a game that’s sort of like the picnic game from before. I give you a series of numbers, and then you give me more series of numbers so that I can confirm or deny that each meets the rule I have in mind. So I say:

And then you offer the following series (serieses?):

60, 90, 120

Each of these series tests a particular hypothesis. The first tests whether the important thing is that the numbers start with 2, 3, and 4. The second tests whether the rule is to add 10 each successive number in the series. The third tests a more complicated hypothesis: add half of the first number to itself to get the second number, then add one third of the second number to itself to get the third number.

Now let’s say I tell you that only the third series is acceptable. What now?

Well, our hypothesis was pretty complex, but it seems pretty good. I can infer that this is the correct rule. Alternatively, I might look at other hypotheses which fit the evidence equally well: 1x, 1.5x, 2x? or maybe it’s 2x, 3x, 4x? What about x, 1.5x, x$^2$? These all make sense of the data, but are they equal apart from that?

Let’s suppose we can’t easily get more data with which to test our various hypotheses. We’ve got 4 to choose from and nothing in the evidence suggests that one of the hypotheses is better than the others—they all fit the evidence perfectly. What do we do?

One thing we could do is choose which hypothesis is best for reasons other than fit with the evidence. Maybe we want a simpler hypothesis, or maybe we want a more elegant hypothesis, or one which suggests more routes for investigation. These are what we might call “theoretical virtues”—they’re the things we want to see in a theory. The process of abduction is the process of selecting the hypothesis that has the most to offer in terms of theoretical virtues: the simplest, most elegant, most fruitful, most general, and so on.

In science in particular, we value a few theoretical virtues over others: support by the empirical evidence available, replicability of the results in a controlled setting by other scientists, ideally mathematical precision or at least a lack of vagueness, and parsimony or simplicity in terms of the sorts of things the hypothesis requires us to believe in.

Confirmation Bias

This is a great opportunity to discuss confirmation bias, or the natural tendency we have to seek out evidence which supports our beliefs and to ignore evidence which gets in the way of our beliefs. We’ll discuss cognitive biases more in Chapter 10, but since we’re dealing with the relationship between evidence and belief, this seems like a good spot to pause and reflect on how our minds work.

The way our minds work naturally, it seems, is to settle on a belief and then work hard to maintain that belief whatever happens. We come to believe that global warming is anthropogenic—is caused by human activities—and then we’re happy to accept a wide variety of evidence for the claim. If the evidence supports our belief, in other words, we don’t take the time or energy to really investigate exactly how convincing that evidence is. If we already believe the conclusion of an inference, in other words, we are much less likely to test or analyze the inference.

Alternatively, when we see pieces of evidence or arguments that appear to point to the contrary, we are either more skeptical of that evidence or more critical of that argument. For instance, if someone notes that the Earth goes through normal cycles of warming and ice ages and warming again, we immediately will look for ways to explain how this warming period is different than others in the past. Or we might look at the period of the cycles to find out if this is happening at the “right” time in our geological history for it not to be caused by humankind. In other words, we’re more skeptical of arguments or evidence that would defeat or undermine our beliefs, but we’re less skeptical and critical of arguments and evidence that supports our beliefs.

Here are some questions to reflect on as you try to decide how guilty you are of confirmation bias in your own reasoning:

Questions for Reflection:

1. Which news sources do you trust? Why?

2. What’s your process for exploring a topic—say a political or scientific or news topic?

3. How do you decide what to believe about a new subject?

4. When other people express an opinion about someone you don’t know, do you withhold judgment? How well do you do so?

5. Are you harder on arguments and evidence that would shake up your beliefs?

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Biology library

Course: biology library > unit 1, the scientific method.

Controlled experiments
The scientific method and experimental design

Introduction

Make an observation.
Ask a question.
Form a hypothesis , or testable explanation.
Make a prediction based on the hypothesis.
Test the prediction.
Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation., 2. ask a question., 3. propose a hypothesis., 4. make predictions., 5. test the predictions..

If the toaster does toast, then the hypothesis is supported—likely correct.
If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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> The Cambridge Handbook of the Imagination
> Hypothetical Thinking

Book contents

The Cambridge Handbook of the Imagination
Copyright page
Contributors
Acknowledgments
1 Surveying the Imagination Landscape
Part I Theoretical Perspectives on the Imagination
Part II Imagery-Based Forms of the Imagination
Part III Intentionality-Based Forms of the Imagination
Part IV Novel Combinatorial Forms of the Imagination
27 On the Interaction Between Episodic and Semantic Representations – Constructing a Unified Account of Imagination
28 How Imagination Supports Narrative Experiences for Textual, Audiovisual, and Interactive Narratives
29 Development of the Fantasy-Reality Distinction
30 Imagining the Real: Buddhist Paths to Wholeness in Tibet
31 Hypothetical Thinking
32 The Counterfactual Imagination: The Impact of Alternatives to Reality on Morality
33 A Look Back at Pioneering Theories of the Creative Brain
Part V Phenomenology-Based Forms of the Imagination
Part VI Altered States of the Imagination
Subject Index

31 - Hypothetical Thinking

from Part IV - Novel Combinatorial Forms of the Imagination

Published online by Cambridge University Press: 26 May 2020

Hypothetical thinking involves imagining possibilities and mentally exploring their consequences. This chapter overviews a contemporary, integrative account of such thinking in the form of Jonathan Evans’s hypothetical thinking theory. This default-interventionist, dual–process theory operates according to three principles: relevance, singularity, and satisficing. To illustrate the explanatory strength of the theory a range of empirical evidence is considered that has arisen from extensive research on hypothesis testing, which involves individuals generating and evaluating hypotheses as they attempt to derive a more general understanding of information. The chapter shows how key findings from hypothesis-testing research undertaken in both laboratory and real-world studies (e.g. in domains such as scientific reasoning) are readily explained by the principles embedded in hypothetical thinking theory. The chapter additionally points to important new directions for future research on hypothetical thinking, including the need for: (1) further studies of real-world hypothesis testing in collaborative contexts, including ones outside of the domain of scientific reasoning; (2) increased neuroscientific analysis of the brain systems underpinning hypothetical thinking so as to inform theoretical developments; and (3) systematic individual-differences investigations to explore the likely association between people’s capacity to think creatively and their ability to engage in effective hypothetical thinking.

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Hypothetical Thinking
By Linden J. Ball
Edited by Anna Abraham , University of Georgia
Book: The Cambridge Handbook of the Imagination
Online publication: 26 May 2020
Chapter DOI: https://doi.org/10.1017/9781108580298.031

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Quantitative research questions	Quantitative research hypotheses
Descriptive research questions	Simple hypothesis
Comparative research questions	Complex hypothesis
Relationship research questions	Directional hypothesis
	Non-directional hypothesis
	Associative hypothesis
	Causal hypothesis
	Null hypothesis
	Alternative hypothesis
	Working hypothesis
	Statistical hypothesis
	Logical hypothesis
	Hypothesis-testing
Qualitative research questions	Qualitative research hypotheses
Contextual research questions	Hypothesis-generating
Descriptive research questions
Evaluation research questions
Explanatory research questions
Exploratory research questions
Generative research questions
Ideological research questions
Ethnographic research questions
Phenomenological research questions
Grounded theory questions
Qualitative case study questions

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Quantitative research questions
Descriptive research question
	- Measures responses of subjects to variables
	- Presents variables to measure, analyze, or assess
	What is the proportion of resident doctors in the hospital who have mastered ultrasonography (response of subjects to a variable) as a diagnostic technique in their clinical training?
Comparative research question
	- Clarifies difference between one group with outcome variable and another group without outcome variable
	Is there a difference in the reduction of lung metastasis in osteosarcoma patients who received the vitamin D adjunctive therapy (group with outcome variable) compared with osteosarcoma patients who did not receive the vitamin D adjunctive therapy (group without outcome variable)?
	- Compares the effects of variables
	How does the vitamin D analogue 22-Oxacalcitriol (variable 1) mimic the antiproliferative activity of 1,25-Dihydroxyvitamin D (variable 2) in osteosarcoma cells?
Relationship research question
	- Defines trends, association, relationships, or interactions between dependent variable and independent variable
	Is there a relationship between the number of medical student suicide (dependent variable) and the level of medical student stress (independent variable) in Japan during the first wave of the COVID-19 pandemic?

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Quantitative research hypotheses
Simple hypothesis
	- Predicts relationship between single dependent variable and single independent variable
	If the dose of the new medication (single independent variable) is high, blood pressure (single dependent variable) is lowered.
Complex hypothesis
	- Foretells relationship between two or more independent and dependent variables
	The higher the use of anticancer drugs, radiation therapy, and adjunctive agents (3 independent variables), the higher would be the survival rate (1 dependent variable).
Directional hypothesis
	- Identifies study direction based on theory towards particular outcome to clarify relationship between variables
	Privately funded research projects will have a larger international scope (study direction) than publicly funded research projects.
Non-directional hypothesis
	- Nature of relationship between two variables or exact study direction is not identified
	- Does not involve a theory
	Women and men are different in terms of helpfulness. (Exact study direction is not identified)
Associative hypothesis
	- Describes variable interdependency
	- Change in one variable causes change in another variable
	A larger number of people vaccinated against COVID-19 in the region (change in independent variable) will reduce the region’s incidence of COVID-19 infection (change in dependent variable).
Causal hypothesis
	- An effect on dependent variable is predicted from manipulation of independent variable
	A change into a high-fiber diet (independent variable) will reduce the blood sugar level (dependent variable) of the patient.
Null hypothesis
	- A negative statement indicating no relationship or difference between 2 variables
	There is no significant difference in the severity of pulmonary metastases between the new drug (variable 1) and the current drug (variable 2).
Alternative hypothesis
	- Following a null hypothesis, an alternative hypothesis predicts a relationship between 2 study variables
	The new drug (variable 1) is better on average in reducing the level of pain from pulmonary metastasis than the current drug (variable 2).
Working hypothesis
	- A hypothesis that is initially accepted for further research to produce a feasible theory
	Dairy cows fed with concentrates of different formulations will produce different amounts of milk.
Statistical hypothesis
	- Assumption about the value of population parameter or relationship among several population characteristics
	- Validity tested by a statistical experiment or analysis
	The mean recovery rate from COVID-19 infection (value of population parameter) is not significantly different between population 1 and population 2.
	There is a positive correlation between the level of stress at the workplace and the number of suicides (population characteristics) among working people in Japan.
Logical hypothesis
	- Offers or proposes an explanation with limited or no extensive evidence
	If healthcare workers provide more educational programs about contraception methods, the number of adolescent pregnancies will be less.
Hypothesis-testing (Quantitative hypothesis-testing research)
	- Quantitative research uses deductive reasoning.
	- This involves the formation of a hypothesis, collection of data in the investigation of the problem, analysis and use of the data from the investigation, and drawing of conclusions to validate or nullify the hypotheses.

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative research questions
Contextual research question
	- Ask the nature of what already exists
	- Individuals or groups function to further clarify and understand the natural context of real-world problems
	What are the experiences of nurses working night shifts in healthcare during the COVID-19 pandemic? (natural context of real-world problems)
Descriptive research question
	- Aims to describe a phenomenon
	What are the different forms of disrespect and abuse (phenomenon) experienced by Tanzanian women when giving birth in healthcare facilities?
Evaluation research question
	- Examines the effectiveness of existing practice or accepted frameworks
	How effective are decision aids (effectiveness of existing practice) in helping decide whether to give birth at home or in a healthcare facility?
Explanatory research question
	- Clarifies a previously studied phenomenon and explains why it occurs
	Why is there an increase in teenage pregnancy (phenomenon) in Tanzania?
Exploratory research question
	- Explores areas that have not been fully investigated to have a deeper understanding of the research problem
	What factors affect the mental health of medical students (areas that have not yet been fully investigated) during the COVID-19 pandemic?
Generative research question
	- Develops an in-depth understanding of people’s behavior by asking ‘how would’ or ‘what if’ to identify problems and find solutions
	How would the extensive research experience of the behavior of new staff impact the success of the novel drug initiative?
Ideological research question
	- Aims to advance specific ideas or ideologies of a position
	Are Japanese nurses who volunteer in remote African hospitals able to promote humanized care of patients (specific ideas or ideologies) in the areas of safe patient environment, respect of patient privacy, and provision of accurate information related to health and care?
Ethnographic research question
	- Clarifies peoples’ nature, activities, their interactions, and the outcomes of their actions in specific settings
	What are the demographic characteristics, rehabilitative treatments, community interactions, and disease outcomes (nature, activities, their interactions, and the outcomes) of people in China who are suffering from pneumoconiosis?
Phenomenological research question
	- Knows more about the phenomena that have impacted an individual
	What are the lived experiences of parents who have been living with and caring for children with a diagnosis of autism? (phenomena that have impacted an individual)
Grounded theory question
	- Focuses on social processes asking about what happens and how people interact, or uncovering social relationships and behaviors of groups
	What are the problems that pregnant adolescents face in terms of social and cultural norms (social processes), and how can these be addressed?
Qualitative case study question
	- Assesses a phenomenon using different sources of data to answer “why” and “how” questions
	- Considers how the phenomenon is influenced by its contextual situation.
	How does quitting work and assuming the role of a full-time mother (phenomenon assessed) change the lives of women in Japan?

Qualitative research hypotheses
Hypothesis-generating (Qualitative hypothesis-generating research)
	- Qualitative research uses inductive reasoning.
	- This involves data collection from study participants or the literature regarding a phenomenon of interest, using the collected data to develop a formal hypothesis, and using the formal hypothesis as a framework for testing the hypothesis.
	- Qualitative exploratory studies explore areas deeper, clarifying subjective experience and allowing formulation of a formal hypothesis potentially testable in a future quantitative approach.

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

Variables	Unclear and weak statement (Statement 1)	Clear and good statement (Statement 2)	Points to avoid
Research question	Which is more effective between smoke moxibustion and smokeless moxibustion?	“Moreover, regarding smoke moxibustion versus smokeless moxibustion, it remains unclear which is more effective, safe, and acceptable to pregnant women, and whether there is any difference in the amount of heat generated.”	1) Vague and unfocused questions
			2) Closed questions simply answerable by yes or no
			3) Questions requiring a simple choice
Hypothesis	The smoke moxibustion group will have higher cephalic presentation.	“Hypothesis 1. The smoke moxibustion stick group (SM group) and smokeless moxibustion stick group (-SLM group) will have higher rates of cephalic presentation after treatment than the control group.	1) Unverifiable hypotheses
		Hypothesis 2. The SM group and SLM group will have higher rates of cephalic presentation at birth than the control group.	2) Incompletely stated groups of comparison
		Hypothesis 3. There will be no significant differences in the well-being of the mother and child among the three groups in terms of the following outcomes: premature birth, premature rupture of membranes (PROM) at < 37 weeks, Apgar score < 7 at 5 min, umbilical cord blood pH < 7.1, admission to neonatal intensive care unit (NICU), and intrauterine fetal death.”	3) Insufficiently described variables or outcomes
Research objective	To determine which is more effective between smoke moxibustion and smokeless moxibustion.	“The specific aims of this pilot study were (a) to compare the effects of smoke moxibustion and smokeless moxibustion treatments with the control group as a possible supplement to ECV for converting breech presentation to cephalic presentation and increasing adherence to the newly obtained cephalic position, and (b) to assess the effects of these treatments on the well-being of the mother and child.”	1) Poor understanding of the research question and hypotheses
Research objective			2) Insufficient description of population, variables, or study outcomes

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

Variables	Unclear and weak statement (Statement 1)	Clear and good statement (Statement 2)	Points to avoid
Research question	Does disrespect and abuse (D&A) occur in childbirth in Tanzania?	How does disrespect and abuse (D&A) occur and what are the types of physical and psychological abuses observed in midwives’ actual care during facility-based childbirth in urban Tanzania?	1) Ambiguous or oversimplistic questions
Research question			2) Questions unverifiable by data collection and analysis
Hypothesis	Disrespect and abuse (D&A) occur in childbirth in Tanzania.	Hypothesis 1: Several types of physical and psychological abuse by midwives in actual care occur during facility-based childbirth in urban Tanzania.	1) Statements simply expressing facts
Hypothesis	Disrespect and abuse (D&A) occur in childbirth in Tanzania.	Hypothesis 2: Weak nursing and midwifery management contribute to the D&A of women during facility-based childbirth in urban Tanzania.	2) Insufficiently described concepts or variables
Research objective	To describe disrespect and abuse (D&A) in childbirth in Tanzania.	“This study aimed to describe from actual observations the respectful and disrespectful care received by women from midwives during their labor period in two hospitals in urban Tanzania.”	1) Statements unrelated to the research question and hypotheses
Research objective			2) Unattainable or unexplorable objectives

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

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Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

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EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

EXAMPLE 1. Descriptive research question (quantitative research)
- Presents research variables to be assessed (distinct phenotypes and subphenotypes)
“BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
EXAMPLE 2. Relationship research question (quantitative research)
- Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
“Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
EXAMPLE 3. Comparative research question (quantitative research)
- Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
“BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
EXAMPLE 4. Exploratory research question (qualitative research)
- Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
“Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
EXAMPLE 5. Relationship research question (quantitative research)
- Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
“Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

EXAMPLE 1. Working hypothesis (quantitative research)
- A hypothesis that is initially accepted for further research to produce a feasible theory
“As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
“In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
EXAMPLE 2. Exploratory hypothesis (qualitative research)
- Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
“We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
“Conclusion
Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
“We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
EXAMPLE 4. Statistical hypothesis (quantitative research)
- An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
“Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
“Statistical Analysis
( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

EXAMPLE 1. Background, hypotheses, and aims are provided
“Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
“ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
“This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
EXAMPLE 2. Background, hypotheses, and aims are provided
“We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
“ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
EXAMPLE 3. Background, aim, and hypothesis are provided
“In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
“This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
“ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

Conceptualization: Barroga E, Matanguihan GJ.
Methodology: Barroga E, Matanguihan GJ.
Writing - original draft: Barroga E, Matanguihan GJ.
Writing - review & editing: Barroga E, Matanguihan GJ.

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What Is Inductive Reasoning? | Definition & Examples

What Is Inductive Reasoning? | Definition & Examples

Published on April 1, 2024 by Julia Merkus, MA . Revised on May 29, 2024.

Inductive reasoning involves making broad generalizations based on specific observations.

Induction is used in various academic and professional settings, as well as informal everyday conversations and tasks. This type of reasoning is especially relevant to problems involving pattern recognition, prediction, and decision-making.

Generalization: Therefore, all swans everywhere are probably white.

This inference might seem reasonable based on the available evidence. However, the sample of swans at the local park is too small to merit such a broad conclusion. Studying a geographically diverse sample would show that there are non-white swans, including the black swans of Australia.

What is inductive reasoning, inductive vs deductive reasoning, inductive reasoning examples, inductive arguments, types of inductive reasoning, inductive reasoning errors, frequently asked questions about inductive reasoning.

Inductive reasoning involves deriving general principles or conclusions from particular examples.

Induction allows us to infer patterns or trends based on specific data and is commonly used in scientific research, data analysis, and everyday decision-making.

It belongs to the broader category of ampliative reasoning , which also includes analogical reasoning (sometimes considered a subcategory of induction) and abductive reasoning.

Whereas in deductive reasoning , conclusions don’t include novel information or hypotheses that aren’t mentioned in the premises, ampliative forms of reasoning yield conclusions that are broader in scope than the premises:

Inductive reasoning : Derives general principles from specific observations or patterns (e.g., observing repeated sunrises and concluding that the sun always rises from the east)
Analogical reasoning : Notes a specific similarity between two entities and infers that additional, related similarities exist (e.g., conceptualizing how the human heart works by comparing it to a water pump)
Abductive reasoning : Explains the most likely cause of a specific observation (e.g., finding a broken window and inferring that it was caused by an attempted burglary)

Inductive reasoning is often defined as the opposite of deductive reasoning . Both ways of thinking are crucial, but they serve different roles:

Example: “Researchers have observed a high recovery rate in patients who received a certain malaria medication. Therefore, there is strong evidence to suggest that this medication effectively treats malaria.”
Example: “By definition, all birds have feathers. Robins are birds. Therefore, robins have feathers.”

Multiple forms of reasoning often intersect in research, with inductive reasoning and deductive reasoning playing different roles:

Inductive reasoning is used to form a hypothesis based on observed data.
Deductive reasoning is used to make specific predictions based on a rule that is assumed to be true.

A researcher observes that many birds are migrating later in the year than they used to. This leads to the hypothesis that climate change is causing changes in migration schedules.

Deductive reasoning :

Examples of inductive reasoning can be found in scientific research, market analysis, public health studies, environmental conservation efforts, and technological innovation, among other fields.

Recognizing a pattern in historical stock market data and using this pattern to predict future market trends
Tracking the frequency of extreme weather events and inferring ongoing climate change trends
Investigating bacterial responses to antibiotics and discovering the mechanisms of antibiotic resistance

Inductive arguments are always considered informal arguments , and they play a prominent role in everyday life. An inductive argument cannot definitively prove its conclusion, but it can provide a strong case for a hypothesis. Inductive arguments are described as either strong or weak.

They are defined in contrast to deductive arguments (or formal arguments ), which are described as either valid or invalid depending on whether they follow one of numerous prescribed forms.

In a strong inductive argument , the premises, if true, make the conclusion highly probable. For example, observing that every ball that is dropped falls to the ground would support a strong generalization about the existence of gravity.

In contrast, a weak inductive argument has premises that, even if true, do not make the conclusion probable. For example, observing that a small, localized group of swans are white would be a weak basis for an argument that all swans everywhere are white.

Inductive reasoning can take a variety of approaches to drawing inferences:

Analogical reasoning : Inferring further similarities between two or more situations or entities based on known shared characteristics (sometimes considered a separate form of reasoning from induction)
Causal inference : Establishing cause-and-effect principles based on observations
Predictive reasoning : Using past patterns or trends to forecast future events or outcomes
Statistical inference : Making predictions or conclusions about a population based on statistical data

Faulty inductive reasoning typically has one or more of the following problems:

Inadequate samples : Drawing conclusions from limited or unrepresentative data
Focus on irrelevant information : Diverting attention from factors directly related to the conclusion
Overgeneralization : Ignoring contradictions or variations in observed phenomena and forming conclusions that fail to consider all relevant contexts
Unwarranted assumptions : Treating debatable claims as true without providing evidence

In the context of argumentation , errors in inductive reasoning sometimes fall into one of many common patterns of reasoning errors known as logical fallacies .

Inductive logical fallacies

Inductive arguments that include reasoning errors are classified as informal logical fallacies if they fall into one of many identified patterns.

Inductive errors never fall into the other main category, formal logical fallacies , which involve breaking rules of formal logic that apply only to deductive arguments.

Informal logical fallacies are errors of content or context that make an argument weak and unpersuasive. Many belong to one or more of the following subcategories:

Examples include the ad hominem fallacy , the straw man fallacy , and the appeal to authority fallacy , among others.
Examples include the equivocation fallacy , the motte and bailey fallacy , and the amphiboly fallacy .
Examples include the circular reasoning fallacy , the false dilemma fallacy , and the begging the question fallacy .

Deductive reasoning is considered stronger than inductive reasoning in a specific sense:

If a deductive argument’s premises are factually correct, and its structure is valid, then its conclusion is guaranteed to be true.

An inductive argument, in contrast, can only suggest the strong likelihood of its conclusion

Inductive reasoning and deductive reasoning are the two most prominent approaches to critical thinking and argumentation. Each plays a crucial role in reasoning and argumentation, but they serve different functions:

Cannot prove a conclusion with absolute certainty
Can result in informal logical fallacies (i.e., errors of content)
Can prove a conclusion with absolute certainty if the premises are true and the argument has a valid form
Can result in formal logical fallacies (i.e., errors of form)

Analogical reasoning is sometimes considered a subcategory of inductive reasoning because it involves generalizing from specific instances to derive broader principles or patterns. However, some argue that analogical reasoning is distinct from induction because it involves drawing conclusions based on similarities between cases rather than generalizing from specific instances.

Along with abductive reasoning , they are forms of ampliative reasoning (in contrast to deductive reasoning ).

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Julia Merkus, MA

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Science and the scientific method: Definitions and examples

Here's a look at the foundation of doing science — the scientific method.

The scientific method

Hypothesis, theory and law, a brief history of science, additional resources, bibliography.

Science is a systematic and logical approach to discovering how things in the universe work. It is also the body of knowledge accumulated through the discoveries about all the things in the universe.

The word "science" is derived from the Latin word "scientia," which means knowledge based on demonstrable and reproducible data, according to the Merriam-Webster dictionary . True to this definition, science aims for measurable results through testing and analysis, a process known as the scientific method. Science is based on fact, not opinion or preferences. The process of science is designed to challenge ideas through research. One important aspect of the scientific process is that it focuses only on the natural world, according to the University of California, Berkeley . Anything that is considered supernatural, or beyond physical reality, does not fit into the definition of science.

When conducting research, scientists use the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis (often in the form of an if/then statement) that is designed to support or contradict a scientific theory .

"As a field biologist, my favorite part of the scientific method is being in the field collecting the data," Jaime Tanner, a professor of biology at Marlboro College, told Live Science. "But what really makes that fun is knowing that you are trying to answer an interesting question. So the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not."

Here's an illustration showing the steps in the scientific method.

The steps of the scientific method go something like this, according to Highline College :

Make an observation or observations.
Form a hypothesis — a tentative description of what's been observed, and make predictions based on that hypothesis.
Test the hypothesis and predictions in an experiment that can be reproduced.
Analyze the data and draw conclusions; accept or reject the hypothesis or modify the hypothesis if necessary.
Reproduce the experiment until there are no discrepancies between observations and theory. "Replication of methods and results is my favorite step in the scientific method," Moshe Pritsker, a former post-doctoral researcher at Harvard Medical School and CEO of JoVE, told Live Science. "The reproducibility of published experiments is the foundation of science. No reproducibility — no science."

Some key underpinnings to the scientific method:

The hypothesis must be testable and falsifiable, according to North Carolina State University . Falsifiable means that there must be a possible negative answer to the hypothesis.
Research must involve deductive reasoning and inductive reasoning . Deductive reasoning is the process of using true premises to reach a logical true conclusion while inductive reasoning uses observations to infer an explanation for those observations.
An experiment should include a dependent variable (which does not change) and an independent variable (which does change), according to the University of California, Santa Barbara .
An experiment should include an experimental group and a control group. The control group is what the experimental group is compared against, according to Britannica .

The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory. While a theory provides an explanation for a phenomenon, a scientific law provides a description of a phenomenon, according to The University of Waikato . One example would be the law of conservation of energy, which is the first law of thermodynamics that says that energy can neither be created nor destroyed.

A law describes an observed phenomenon, but it doesn't explain why the phenomenon exists or what causes it. "In science, laws are a starting place," said Peter Coppinger, an associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology. "From there, scientists can then ask the questions, 'Why and how?'"

Laws are generally considered to be without exception, though some laws have been modified over time after further testing found discrepancies. For instance, Newton's laws of motion describe everything we've observed in the macroscopic world, but they break down at the subatomic level.

This does not mean theories are not meaningful. For a hypothesis to become a theory, scientists must conduct rigorous testing, typically across multiple disciplines by separate groups of scientists. Saying something is "just a theory" confuses the scientific definition of "theory" with the layperson's definition. To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science.

This Copernican heliocentric solar system, from 1708, shows the orbit of the moon around the Earth, and the orbits of the Earth and planets round the sun, including Jupiter and its moons, all surrounded by the 12 signs of the zodiac.

The earliest evidence of science can be found as far back as records exist. Early tablets contain numerals and information about the solar system , which were derived by using careful observation, prediction and testing of those predictions. Science became decidedly more "scientific" over time, however.

1200s: Robert Grosseteste developed the framework for the proper methods of modern scientific experimentation, according to the Stanford Encyclopedia of Philosophy. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing.

1400s: Leonardo da Vinci began his notebooks in pursuit of evidence that the human body is microcosmic. The artist, scientist and mathematician also gathered information about optics and hydrodynamics.

1500s: Nicolaus Copernicus advanced the understanding of the solar system with his discovery of heliocentrism. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.

1600s: Johannes Kepler built upon those observations with his laws of planetary motion. Galileo Galilei improved on a new invention, the telescope, and used it to study the sun and planets. The 1600s also saw advancements in the study of physics as Isaac Newton developed his laws of motion.

1700s: Benjamin Franklin discovered that lightning is electrical. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry , developed the law of conservation of mass.

1800s: Milestones included Alessandro Volta's discoveries regarding electrochemical series, which led to the invention of the battery. John Dalton also introduced atomic theory, which stated that all matter is composed of atoms that combine to form molecules. The basis of modern study of genetics advanced as Gregor Mendel unveiled his laws of inheritance. Later in the century, Wilhelm Conrad Röntgen discovered X-rays , while George Ohm's law provided the basis for understanding how to harness electrical charges.

1900s: The discoveries of Albert Einstein , who is best known for his theory of relativity, dominated the beginning of the 20th century. Einstein's theory of relativity is actually two separate theories. His special theory of relativity, which he outlined in a 1905 paper, " The Electrodynamics of Moving Bodies ," concluded that time must change according to the speed of a moving object relative to the frame of reference of an observer. His second theory of general relativity, which he published as " The Foundation of the General Theory of Relativity ," advanced the idea that matter causes space to curve.

In 1952, Jonas Salk developed the polio vaccine , which reduced the incidence of polio in the United States by nearly 90%, according to Britannica . The following year, James D. Watson and Francis Crick discovered the structure of DNA , which is a double helix formed by base pairs attached to a sugar-phosphate backbone, according to the National Human Genome Research Institute .

2000s: The 21st century saw the first draft of the human genome completed, leading to a greater understanding of DNA. This advanced the study of genetics, its role in human biology and its use as a predictor of diseases and other disorders, according to the National Human Genome Research Institute .

This video from City University of New York delves into the basics of what defines science.
Learn about what makes science science in this book excerpt from Washington State University .
This resource from the University of Michigan — Flint explains how to design your own scientific study.

Merriam-Webster Dictionary, Scientia. 2022. https://www.merriam-webster.com/dictionary/scientia

University of California, Berkeley, "Understanding Science: An Overview." 2022. https://undsci.berkeley.edu/article/0_0_0/intro_01

Highline College, "Scientific method." July 12, 2015. https://people.highline.edu/iglozman/classes/astronotes/scimeth.htm

North Carolina State University, "Science Scripts." https://projects.ncsu.edu/project/bio183de/Black/science/science_scripts.html

University of California, Santa Barbara. "What is an Independent variable?" October 31,2017. http://scienceline.ucsb.edu/getkey.php?key=6045

Encyclopedia Britannica, "Control group." May 14, 2020. https://www.britannica.com/science/control-group

The University of Waikato, "Scientific Hypothesis, Theories and Laws." https://sci.waikato.ac.nz/evolution/Theories.shtml

Stanford Encyclopedia of Philosophy, Robert Grosseteste. May 3, 2019. https://plato.stanford.edu/entries/grosseteste/

Encyclopedia Britannica, "Jonas Salk." October 21, 2021. https://www.britannica.com/ biography /Jonas-Salk

National Human Genome Research Institute, "Phosphate Backbone." https://www.genome.gov/genetics-glossary/Phosphate-Backbone

National Human Genome Research Institute, "What is the Human Genome Project?" https://www.genome.gov/human-genome-project/What

‌ Live Science contributor Ashley Hamer updated this article on Jan. 16, 2022.

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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

proposition
supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

counter - hypothesis
nebular hypothesis
null hypothesis
planetesimal hypothesis
Whorfian hypothesis

Articles Related to hypothesis

This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

Dictionary Entries Near hypothesis

hypothermia

hypothesize

Cite this Entry

“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 10 Jun. 2024.

Kids Definition

Kids definition of hypothesis, medical definition, medical definition of hypothesis, more from merriam-webster on hypothesis.

Nglish: Translation of hypothesis for Spanish Speakers

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Britannica.com: Encyclopedia article about hypothesis

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“Inductive” vs. “Deductive”: How To Reason Out Their Differences

What Does Inductive Mean?
What Does Deductive Mean?
Inductive Reasoning Vs. Deductive Reasoning

Inductive and deductive are commonly used in the context of logic, reasoning, and science. Scientists use both inductive and deductive reasoning as part of the scientific method . Fictional detectives like Sherlock Holmes are famously associated with methods of deduction (though that’s often not what Holmes actually uses—more on that later). Some writing courses involve inductive and deductive essays.

But what’s the difference between inductive and deductive ? Broadly speaking, the difference involves whether the reasoning moves from the general to the specific or from the specific to the general. In this article, we’ll define each word in simple terms, provide several examples, and even quiz you on whether you can spot the difference.

⚡ Quick summary

Inductive reasoning (also called induction ) involves forming general theories from specific observations. Observing something happen repeatedly and concluding that it will happen again in the same way is an example of inductive reasoning. Deductive reasoning (also called deduction ) involves forming specific conclusions from general premises, as in: everyone in this class is an English major; Jesse is in this class; therefore, Jesse is an English major.

What does inductive mean?

Inductive is used to describe reasoning that involves using specific observations, such as observed patterns, to make a general conclusion. This method is sometimes called induction . Induction starts with a set of premises , based mainly on experience or experimental evidence. It uses those premises to generalize a conclusion .

For example, let’s say you go to a cafe every day for a month, and every day, the same person comes at exactly 11 am and orders a cappuccino. The specific observation is that this person has come to the cafe at the same time and ordered the same thing every day during the period observed. A general conclusion drawn from these premises could be that this person always comes to the cafe at the same time and orders the same thing.

While inductive reasoning can be useful, it’s prone to being flawed. That’s because conclusions drawn using induction go beyond the information contained in the premises. An inductive argument may be highly probable , but even if all the observations are accurate, it can lead to incorrect conclusions.

Follow up this discussion with a look at concurrent vs. consecutive .

In our basic example, there are a number of reasons why it may not be true that the person always comes at the same time and orders the same thing.

Additional observations of the same event happening in the same way increase the probability that the event will happen again in the same way, but you can never be completely certain that it will always continue to happen in the same way.

That’s why a theory reached via inductive reasoning should always be tested to see if it is correct or makes sense.

What else does inductive mean?

Inductive can also be used as a synonym for introductory . It’s also used in a more specific way to describe the scientific processes of electromagnetic and electrostatic induction —or things that function based on them.

What does deductive mean?

Deductive reasoning (also called deduction ) involves starting from a set of general premises and then drawing a specific conclusion that contains no more information than the premises themselves. Deductive reasoning is sometimes called deduction (note that deduction has other meanings in the contexts of mathematics and accounting).

Here’s an example of deductive reasoning: chickens are birds; all birds lay eggs; therefore, chickens lay eggs. Another way to think of it: if something is true of a general class (birds), then it is true of the members of the class (chickens).

Deductive reasoning can go wrong, of course, when you start with incorrect premises. For example, look where this first incorrect statement leads us: all animals that lay eggs are birds; snakes lay eggs; therefore, snakes are birds.

The scientific method can be described as deductive . You first formulate a hypothesis —an educated guess based on general premises (sometimes formed by inductive methods). Then you test the hypothesis with an experiment . Based on the results of the experiment, you can make a specific conclusion as to the accuracy of your hypothesis.

You may have deduced there are related terms to this topic. Start with a look at interpolation vs. extrapolation .

Deductive reasoning is popularly associated with detectives and solving mysteries. Most famously, Sherlock Holmes claimed to be among the world’s foremost practitioners of deduction , using it to solve how crimes had been committed (or impress people by guessing where they had been earlier in the day).

However, despite this association, reasoning that’s referred to as deduction in many stories is actually more like induction or a form of reasoning known as abduction , in which probable but uncertain conclusions are drawn based on known information.

Sherlock’s (and Arthur Conan Doyle ’s) use of the word deduction can instead be interpreted as a way (albeit imprecise) of referring to systematic reasoning in general.

What is the difference between inductive vs. deductive reasoning?

Inductive reasoning involves starting from specific premises and forming a general conclusion, while deductive reasoning involves using general premises to form a specific conclusion.

Conclusions reached via deductive reasoning cannot be incorrect if the premises are true. That’s because the conclusion doesn’t contain information that’s not in the premises. Unlike deductive reasoning, though, a conclusion reached via inductive reasoning goes beyond the information contained within the premises—it’s a generalization , and generalizations aren’t always accurate.

The best way to understand the difference between inductive and deductive reasoning is probably through examples.

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Examples of inductive and deductive reasoning

Examples of inductive reasoning.

Premise: All known fish species in this genus have yellow fins. Conclusion: Any newly discovered species in the genus is likely to have yellow fins.

Premises: This volcano has erupted about every 500 years for the last 1 million years. It last erupted 499 years ago. Conclusion: It will erupt again soon.

Examples of deductive reasoning

Premises: All plants with rainbow berries are poisonous. This plant has rainbow berries. Conclusion: This plant is poisonous.

Premises: I am lactose intolerant. Lactose intolerant people get sick when they consume dairy. This milkshake contains dairy. Conclusion: I will get sick if I drink this milkshake.

Reason your way to the best score by taking our quiz on "inductive" vs. "deductive" reasoning!

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The Economics of Administration Action on Student Debt

Higher education financing allows many Americans from lower- and middle-income backgrounds to invest in education. However, over the past 30 years, college tuition prices have increased faster than median incomes, leaving many Americans with large amounts of student debt that they struggle or are unable to, pay off.

Recognizing the burden of this debt, the Biden-Harris Administration has pursued two key strategies for debt reduction and cancellation. The first, student debt relief (SDR), aims to address the ill effects of flaws in the student debt system for borrowers. The second, the SAVE plan, reforms the federal student loan system, improving student loan affordability for future students and providing current graduates with breathing room during the beginning of a new career.

This issue brief examines the factors that precipitated the current student debt landscape, and details how both SDR and SAVE will enhance the economic status of millions of Americans with student debt: enabling them to allocate more funds towards basic necessities, take career risks, start businesses, and purchase homes. This brief highlights credible research, underscoring how the Administration’s student debt relief could boost consumption in the short-term by billions of dollars and could have important impacts on borrower mental health, financial security, and outcomes such as homeownership and entrepreneurship. This brief also details how the SAVE plan makes repaying college costs more affordable for current borrowers and future generations. CEA simulations show that, under SAVE, an average borrower with a bachelor’s degree could save $20,000 in loan payments, while a borrower with an associate degree could see nearly 90 percent savings compared to the standard loan repayment plan. These changes enable more people to pursue education and contribute to the broader economy.

Why do borrowers need relief?

Over the last 20 years especially, the sticker price of college has risen significantly. Despite recent minor declines, sticker prices at public universities (which over 70% of undergraduate students in the United States attend) are 56% higher today than two decades ago. [1] While there are many reasons for this trend, the most rapid increases in tuition often occur during economic downturns as tuitions grow to fill the budgetary holes that are left when states cut their support to public colleges ( Webber, 2017 ; Deming and Walters 2018 ). This is especially problematic given many people choose to return to school during economic downturns ( Betts and MacFarland 1995 ; Hillman and Orians 2013 ). Unfortunately, contracting state appropriations have played a role in shifting the responsibility of financing away from public subsidies and toward students and families ( Turner and Barr, 2013 ; Bound et al., 2019 )–leading many students to take on more debt.

At the same time that college sticker prices have risen, the wage premium (the earnings difference between college goers and high school graduates) has not seen analogous growth. While obtaining a college degree remains a reliable entry point to the middle class, the relative earning gains for degree holders began to stagnate in the early 2000s after increasing for several decades. As shown in Figure 1b, since 2000, the wage premium for both bachelor’s degree holders and those with “some college” education (which includes anyone who enrolled in college but didn’t earn a BA) saw declines around the 2001-02 and 2008-10 recessions and a slow, inconsistent recovery thereafter. The decline is particularly notable for students who didn’t complete a four-year degree, a group that includes two-year college enrollees who have among the highest student loan default rates. [2]

Traditional economic theory tells us that individuals choose to invest in post-secondary education based on the expected costs and wage returns associated with the investment. But rapid and unforeseeable rises in prices and declines in college wage premia have contributed to decades of “unlucky” college-entry cohorts affected by a form of recessionary scarring . For example, a student who entered college in 2006 would have expected a sticker price of roughly $8,800 per year for a four-year college, but actually faced tuition of over $10,000 in their final year of college, a roughly 15% difference. This same student, upon graduation if they worked full time, would have earned about $3,500 less, on average, than what they would have expected upon entering. This example illustrates that many borrowers made sound borrowing decisions with available information, but as a result of these trends ended up with more debt than they could afford to pay off. [3] Consistent with this notion, the default rate for “unlucky” college entry cohorts of the 2000s is much higher than those of other cohorts, with undergraduate default rates doubling between 2000 and 2010: in 2017, 21 percent of undergraduate loan holders and 6 percent of graduate loan holders defaulted within 3 years ( CBO, 2020 ).

It is important to note that sticker prices for public institutions have declined 7 percent since 2021, the same period over which college wage premiums have been rising. Declining tuition, for the first time in decades, coincided with increased investment in higher education through pandemic-era legislation such as the American Rescue Plan, which allocated $40 billion in 2021 to support institutes of higher education and their students. Despite these improvements, as well as significant advances in the return on college investments over the last three years, many current borrowers still need some relief. The Administration has taken significant action to protect future cohorts from similar risks.

How the Administration is providing relief

Retrospective: Student Debt Relief Helps Existing Borrowers

In a commitment to help those who are overburdened with debt, the Administration has already approved Federal student debt cancellation for nearly 4 million Americans through various actions. Today , the Administration announced details of proposed rules that, if finalized as proposed, would provide relief to over 30 million borrowers when taken together with actions to date.

Importantly, much of this debt forgiveness comes from correcting program administration and improving regulations related to laws that were on the books before this Administration took office. This debt relief has affected borrowers from all walks of life, including nearly 900,000 Americans who have dedicated their lives to public service (such as teachers, social workers, nurses, firefighters, police officers, and others), borrowers who were misled and cheated by their institutions, and borrowers who are facing total or permanent disability, including many veterans. By relieving these borrowers of long-held, and in some cases very large burdens of debt, relief can have significant meaning and impact for borrowers, families, and their communities.

By reducing debtors’ liabilities, debt relief raises net worth (assets, including income less liabilities). Debt relief can also ease the financial burden of making payments—leading to greater disposable income for borrowers and their families, which enhances living standards and could positively influence decisions about employment, home buying, and mobility. While there are few direct estimates of the effect of debt cancelation in the literature, estimates based on the relationship between wealth and consumption suggest that this forgiveness could increase consumption by several billions of dollars each year in the next five to ten years.

Additionally, a recent study suggests that student debt cancellation can lead to increased earnings (due to greater geographic and career mobility), improved credit scores, and lower delinquency rates on other debts ( Di Maggio, Kalda, and Yao, 2019 ). This can facilitate access to capital for starting a business or buying a car or home. As home mortgages often require a certain debt-to-income ratio and depend heavily on credit scores, student debt cancellation could potentially increase home ownership. Indeed, based on the mechanical relationship between housing industry affordability standards and debt-to-income ratios, industry sources have suggested that those without student debt could afford to take out substantially larger mortgages ( Zillow, 2018 ). Other research also indicates a negative correlation between student loan debt and homeownership ( Mezza et al., 2020 ).

It is important to note that, while these pecuniary benefits are important, the benefits associated with debt relief are not merely financial. Experimental evidence has linked holding debt to heightened levels of stress and anxiety ( Drentea and Reynolds, 2012 ), worse self-reported physical health ( Sweet et al., 2013 ), and reduced cognitive capacity ( Robb et al., 2012 ; Ong et al., 2019 ). Studies also show that holding student debt can be a barrier to positive life cycle outcomes such as entrepreneurship ( Krishnan and Wang, 2019 ), and marriage ( Gicheva, 2016 ; Sieg and Wang, 2018 ). Student debt relief has the potential to improve these key outcomes for millions of borrowers.

Prospective: The SAVE Plan Helps Prevent Future Challenges

To address unaffordable education financing moving forward, the Administration has also introduced the Saving on a Valuable Education (SAVE) loan repayment program. The SAVE plan prospectively helps student borrowers by ensuring that once they graduate, they never have to pay more than they can afford towards their student loan debt. Importantly, the SAVE plan protects borrowers from being “unlucky” by ensuring that high tuition or low earnings do not result in loan payments that borrowers can’t afford. The CEA has detailed the real benefits of SAVE for borrowers in issue briefs and blogs , underscoring that SAVE is the most affordable student loan repayment program in U.S. history. By substantially reducing monthly payment amounts compared to previous income driven repayment (IDR) plans and reducing time to forgiveness to as little as 10 years for people who borrowed smaller amounts, the SAVE plan can mean tens of thousands of dollars in real savings for borrowers over the course of repayment.

Figure 2 gives the example of two representative borrowers. Take the first, a 4-year college graduate who has $31,000 in debt and earns about $40,500 per year. Under a standard repayment plan, this borrower would pay roughly $330 dollars each month for 10 years. Under SAVE, this borrower would pay about $50 per month for the first ten years, and on average about $130 per month for the next 10 years. Over a 20-year period, this borrower would make roughly $17,500 less in payments, not accounting for inflation over that period. This represents a 56 percent reduction in total payments compared to the standard repayment plan and includes considerable loan forgiveness. Similarly, the representative 2-year college graduate has $10,000 in debt and earns about $32,000 per year. Under a standard plan, this borrower would pay $110 dollars each month for 10 years. Under SAVE, this borrower would pay $0 per month for the first two years, and under $20 per month for the next eight years before their debt is forgiven at year 10. Overall, this borrower would be responsible for roughly $11,700 less in lifetime payments, not accounting for inflation. This borrower sees nearly 90 percent savings compared to the standard plan and receives considerable loan forgiveness.

SAVE can also have benefits beyond the individual borrower. More money in borrowers’ pockets due to lower payment obligations under SAVE could boost consumption and give borrowers breathing room to make payments on other debt. This consumption effect is bolstered by a large literature documenting the benefits of easing liquidity constraints (see, for example, Aydin, 2022 ; Parker et al., 2022 ). Additionally, by shortening time to forgiveness for undergraduate borrowers, SAVE can lead to positive debt-relief outcomes (as discussed above) for many more borrowers.

Another key aspect of income-driven repayment plans like SAVE is that they protect borrowers from having to make large payments when incomes are low. Specifically, the required payments are not based on the initial loan balance, but on one’s income and household size so that those cohorts who need to borrow more to pay for college do not make larger payments unless they make more income. SAVE also protects more of a borrower’s income as discretionary and, when the full plan is implemented in Summer 2024, will limit monthly payments on undergraduate loans to 5 percent of discretionary income. In fact, for single borrowers who make less than $33,000 per year, the required monthly payments will be zero dollars. From a finance perspective, the SAVE plan provides a form of insurance against tuition spikes and economic downturns–taking some of the risk out of investing in one’s education while also bringing costs down.

A common concern, and one that could mute these benefits, is that increases in the generosity of education financing may encourage institutions to raise tuition and fees in response, a phenomenon commonly referred to as the Bennett Hypothesis (for an excellent overview of research, see Dynarski et al., 2022 ). Theoretically, in a market when sellers are maximizing profits, any policy that increases demand will also increase prices. However, this is less likely to impact the over 70% of U.S. undergraduates who attend public colleges, which are not profit-driven and often have statutorily set tuition. Consistent with this notion, the evidence in support of the Bennett Hypothesis primarily comes from for-profit colleges, which are highly reliant on students who receive federal financial aid ( Cellini and Goldin, 2014 ; Baird et al, 2022 ). [4] Importantly, although the for-profit sector enrolls some of the country’s most vulnerable students, enrollment in the sector in 2021 accounted for only 5 percent of total undergraduate enrollment, suggesting that aggregate tuition increases in response to changes in education financing may be modest. Furthermore, the Biden-Harris Administration has taken action to crack down on for-profit colleges that take advantage of, or mislead, their students. And, recent regulations, such as the Gainful Employment ( GE ) rule, add safeguards against unaffordable debt regardless of more generous education financing.

Although the SAVE plan stands to benefit borrowers of all backgrounds, the plan has important racial and socioeconomic equity implications because it is particularly beneficial for those borrowers with the lowest incomes. Centuries of inequities have led to Black, Hispanic, and Native households being more likely than their White peers to fall in the low end of the income distribution. This means that, mechanically, the SAVE plan’s benefits could accrue disproportionately to these groups. Indeed, using completion data from recent years, an Urban Institute analysis estimates that 59 percent of credentials earned by Black students and 53 percent of credentials earned by Hispanic students are likely to be eligible for some amount of loan forgiveness under SAVE, compared to 42 percent of credentials earned by White students ( Delisle and Cohn, 2023 ). Finally, the interest subsidy described in an August 2023 CEA blog , prevents ballooning balances when a borrower cannot cover their entire monthly interest payment, a phenomenon that has historically led to many borrowers in general, and Black borrowers in particular, to see loan balances that are higher than their original loan amount, even several years out from graduating with a bachelor’s degree ( NCES, 2023 ).

Broader economic impacts

The benefits associated with SDR and SAVE for millions of Americans are considerable. In the short run, under both SDR and SAVE, those who receive relief may be able to spend more in their communities and contribute to their local economies. Summing the likely consumption effects of the Administration’s student debt relief and SAVE programs results in billions of dollars in additional consumption annually. Despite the modest effect on the macroeconomy as a whole (note that the U.S. economy is roughly $28 trillion with a population of roughly 320 million), these consumption effects represent incredibly meaningful impacts on individual borrowers’ financial security and the economic wellbeing of their communities.

SAVE, because it brings down the cost of taking out loans to go to college, has the potential to lead to longer-term economic growth if it leads to greater educational attainment. This increased attainment can occur both through improved retention and completion of post-secondary education, and also the movement of students into college who would not have otherwise enrolled. There is a long macroeconomics literature linking educational attainment in a nation to GDP growth (see, for example, Lucas, 1988 ; Hanushek and Woesmann, 2008 ). While identifying the causal effect of schooling on GDP is challenging, researchers, using a variety of approaches, find that a one-year increase in average education (for the entire working population) would increase the real GDP level by between 5 and 12 percent ( Barro and Lee, 2013 ; Soto, 2002 ) —a result that is in line with the micro-founded relationship between years of education and earnings ( Lovenheim and Smith, 2022 ).

To put this relationship in perspective and highlight the growth potential of increasing educational attainment, the CEA simulated the hypothetical effect on GDP of increasing the college-going rate by 1, 3, and 5 percentage points, respectively. This range represents the kinds of changes in college going that have been observed over several years: the college enrollment rate for 18- to 24-year-olds declined 4 percentage points between 2011 and 2021 after increasing by 6 percentage points between 2000 and 2011 ( NCES 2023 ). CEA simulations show that by 2055, a policy that increased the college going rate by 1, 3, and 5 percentage points could increase the level of GDP in 2055 (thirty years from now) by 0.2, 0.6, and 1 percent respectively. This represents hundreds of billions of dollars of additional economic activity in the long run.

While increased growth is an exciting possibility, it would only occur insofar as SAVE leads to increased educational attainment, which is uncertain. The academic literature has found that student loans can promote academic performance ( Barr, et. al. 2021 ), and increase educational attainment by increasing transfers from 2-year to 4-year colleges and increasing college completion among enrollees ( Marx and Turner, 2019 ). At the same time, increases in college-going due to SAVE are by no means guaranteed. While, historically, policies that reduce the cost of college through direct means—such as providing students with generous grant aid, or reducing tuition—have succeeded at raising college enrollment levels ( Dynarski, 2003 ; Turner, 2011 ), a pair of recent studies show that prospective students may only respond to cost changes when they are salient, i.e., they are framed and marketed in the right way ( Dynarski et al., 2021 ), and relatively certain ( Burland et al., 2022 ). However, evidence suggests that there is demand for plans like SAVE ( Balakrishnan et al., 2024 ), particularly as SAVE can provide sizable benefits to borrowers in terms of reducing their long-term debt burden and keep monthly payments low (dependent on a borrower’s income) after they finish school.

This highlights the importance of communicating the benefits of the SAVE program to prospective students who otherwise would not enroll in college due to cost concerns, including potential barriers to paying off student loans in the future. Doing so could lead to meaningful increases in college enrollment, and the resulting improvements in productive capacity could increase the size of the U.S. economy for years to come.

Concluding remarks

The Biden-Harris Administration has taken bold action to address a student debt problem that has been decades in the making. This student debt cancellation will provide well-deserved relief for borrowers who have paid their fair share, many of whom had the proverbial rug pulled out from under them with concurrent rapidly rising tuition and declining returns to a college degree. The relief has and will improve economic health and wellbeing of those who have devoted years of their life to public service, those who were defrauded or misled by their institutions, and those who have been doing all they can to make payments, but have still seen their loan balances grow. Looking to future generations, the Administration implemented the SAVE plan to protect borrowers against tuition spikes and poorer than expected labor market outcomes that often plague students graduating into a period of economic downturn ( Rothstein, 2021 ; Schwandt and von Wachter, 2023 ).

Both student debt relief and SAVE will enhance the economic status of millions of Americans with student debt: enable them to allocate more funds towards basic necessities, take career risks, start businesses, and purchase homes with the understanding that they will never have to pay more than they can afford towards their student loans. Moreover, the SAVE plan makes repayment more affordable for future generations, which helps borrowers manage monthly payments, but also enables more people from all walks of life to explore their full potential and pursue higher education, enhancing the potential of the U.S. workforce and the economy more broadly.

[1] In 2021, 51% of total undergraduates attended public 4-year universities and 21% attended public 2-years in 2021.

[2] The BA group excludes those with a graduate degree, or any education beyond a bachelor’s degree.

[3] Recent research shows that, despite a positive return on investment (ROI) for many, including the average student, the distribution of ROI has widened over the last several decades such that the likelihood of negative ROI is higher than it has historically been, particularly so for underrepresented minority students ( Webber 2022 ).

[4] There is also some evidence in support of the Bennett Hypothesis at the graduate level ( Black et al. 2023 ).

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How to Write a Hypothesis [31 Tips + Examples]

Writing a Hypothesis: The Basics

31 Tips for Writing a Hypothesis

Tip 1: Start with a Clear Research Question

Tip 2: Do Background Research

Tip 3: Identify Independent and Dependent Variables

Tip 4: Make Your Hypothesis Testable

Tip 5: Be Specific and Concise

Tip 6: Choose Simple Language

Tip 7: Formulate a Null Hypothesis

Tip 8: State Alternative Hypotheses

Tip 9: Use “If…Then” Statements

Tip 10: Avoid Assumptions

Tip 11: Review Existing Literature

Tip 12: Consider Multiple Variables

Tip 13: Review Ethical Considerations

Tip 14: Test with Pilot Studies

Tip 15: Build Hypotheses on Existing Theories

Tip 16: Address Real-World Problems

Tip 17: Aim for Clear, Measurable Outcomes

Tip 18: Stay Open to Unexpected Results

Tip 19: Keep Hypotheses Relevant

Tip 20: Collaborate with Peers

Tip 21: Re-evaluate Hypotheses Periodically

Tip 22: Develop Compelling Visuals

Tip 23: Refine Your Data Collection Plan

Tip 24: Focus on Logical Progression

Tip 25: Test Against Diverse Samples

Tip 26: Use Control Groups

Tip 27: Consider Practical Constraints

Tip 28: Recognize Bias Risks

Tip 29: Prepare for Peer Review

Tip 30: Create a Research Proposal

Tip 31: Document Your Findings

Hypothesis Examples for Different Situations

Professional Contacts

Super Personal

Final Thoughts: How to Write a Hypothesis

How to Write a Great Hypothesis

Hypothesis Format

Hypothesis Types

At a Glance

The Hypothesis in the Scientific Method

Elements of a Good Hypothesis

How to Formulate a Good Hypothesis

The Importance of Operational Definitions

Replicability

Hypothesis Checklist

A few examples of simple hypotheses:

Examples of a complex hypothesis include:

Examples of a null hypothesis include:

Examples of an alternative hypothesis:

Collecting Data on Your Hypothesis

Descriptive Research Methods

Experimental Research Methods

What is a Research Hypothesis: How to Write it, Types, and Examples

What is a hypothesis ?

What is a research hypothesis ?

Characteristics of a good hypothesis

How to create an effective research hypothesis

How to write a research hypothesis

Research hypothesis checklist

Types of research hypothesis

1. Null hypothesis:

2. Alternative hypothesis:

3. Directional hypothesis :

4. Non-directional hypothesis:

5. Simple hypothesis :

6 . Complex hypothesis :

7. Associative hypothesis:

8 . Causal hypothesis:

Research hypothesis examples

Importance of testable hypothesis

Frequently Asked Questions (FAQs) on research hypothesis

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