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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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."

• How it works

How to Write a Hypothesis – Steps & Tips

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 26, 2023

What is a Research Hypothesis?

You can test a research statement with the help of experimental or theoretical research, known as a hypothesis.

If you want to find out the similarities, differences, and relationships between variables, you must write a testable hypothesis before compiling the data, performing analysis, and generating results to complete.

The data analysis and findings will help you test the hypothesis and see whether it is true or false. Here is all you need to know about how to write a hypothesis for a  dissertation .

Research Hypothesis Definition

Not sure what the meaning of the research hypothesis is?

A research hypothesis predicts an answer to the research question  based on existing theoretical knowledge or experimental data.

Some studies may have multiple hypothesis statements depending on the research question(s).  A research hypothesis must be based on formulas, facts, and theories. It should be testable by data analysis, observations, experiments, or other scientific methodologies that can refute or support the statement.

Variables in Hypothesis

Developing a hypothesis is easy. Most research studies have two or more variables in the hypothesis, particularly studies involving correlational and experimental research. The researcher can control or change the independent variable(s) while measuring and observing the independent variable(s).

“How long a student sleeps affects test scores.”

In the above statement, the dependent variable is the test score, while the independent variable is the length of time spent in sleep. Developing a hypothesis will be easy if you know your research’s dependent and independent variables.

Once you have developed a thesis statement, questions such as how to write a hypothesis for the dissertation and how to test a research hypothesis become pretty straightforward.

Looking for dissertation help?

Researchprospect to the rescue then.

We have expert writers on our team who are skilled at helping students with quantitative dissertations across a variety of STEM disciplines. Guaranteeing 100% satisfaction!

Step-by-Step Guide on How to Write a Hypothesis

Here are the steps involved in how to write a hypothesis for a dissertation.

Step 1: Start with a Research Question

• Begin by asking a specific question about a topic of interest.
• This question should be clear, concise, and researchable.

Example: Does exposure to sunlight affect plant growth?

Step 2: Do Preliminary Research

• Before formulating a hypothesis, conduct background research to understand existing knowledge on the topic.
• Familiarise yourself with prior studies, theories, or observations related to the research question.

Step 3: Define Variables

• Independent Variable (IV): The factor that you change or manipulate in an experiment.
• Dependent Variable (DV): The factor that you measure.

Example: IV: Amount of sunlight exposure (e.g., 2 hours/day, 4 hours/day, 8 hours/day) DV: Plant growth (e.g., height in centimetres)

Step 4: Formulate the Hypothesis

• A hypothesis is a statement that predicts the relationship between variables.
• It is often written as an “if-then” statement.

Example: If plants receive more sunlight, then they will grow taller.

Step 5: Ensure it is Testable

A good hypothesis is empirically testable. This means you should be able to design an experiment or observation to test its validity.

Example: You can set up an experiment where plants are exposed to varying amounts of sunlight and then measure their growth over a period of time.

Step 6: Consider Potential Confounding Variables

• Confounding variables are factors other than the independent variable that might affect the outcome.
• It is important to identify these to ensure that they do not skew your results.

Example: Soil quality, water frequency, or type of plant can all affect growth. Consider keeping these constant in your experiment.

Step 7: Write the Null Hypothesis

• The null hypothesis is a statement that there is no effect or no relationship between the variables.
• It is what you aim to disprove or reject through your research.

Example: There is no difference in plant growth regardless of the amount of sunlight exposure.

Step 8: Test your Hypothesis

Design an experiment or conduct observations to test your hypothesis.

Example: Grow three sets of plants: one set exposed to 2 hours of sunlight daily, another exposed to 4 hours, and a third exposed to 8 hours. Measure and compare their growth after a set period.

Step 9: Analyse the Results

After testing, review your data to determine if it supports your hypothesis.

Step 10: Draw Conclusions

• Based on your findings, determine whether you can accept or reject the hypothesis.
• Remember, even if you reject your hypothesis, it’s a valuable result. It can guide future research and refine questions.

Three Ways to Phrase a Hypothesis

Try to use “if”… and “then”… to identify the variables. The independent variable should be present in the first part of the hypothesis, while the dependent variable will form the second part of the statement. Consider understanding the below research hypothesis example to create a specific, clear, and concise research hypothesis;

If an obese lady starts attending Zomba fitness classes, her health will improve.

In academic research, you can write the predicted variable relationship directly because most research studies correlate terms.

The number of Zomba fitness classes attended by the obese lady has a positive effect on health.

If your research compares two groups, then you can develop a hypothesis statement on their differences.

An obese lady who attended most Zumba fitness classes will have better health than those who attended a few.

How to Write a Null Hypothesis

If a statistical analysis is involved in your research, then you must create a null hypothesis. If you find any relationship between the variables, then the null hypothesis will be the default position that there is no relationship between them. H0 is the symbol for the null hypothesis, while the hypothesis is represented as H1. The null hypothesis will also answer your question, “How to test the research hypothesis in the dissertation.”

H0: The number of Zumba fitness classes attended by the obese lady does not affect her health.

H1: The number of Zumba fitness classes attended by obese lady positively affects health.

Also see:  Your Dissertation in Education

Hypothesis Examples

Research Question: Does the amount of sunlight a plant receives affect its growth? Hypothesis: Plants that receive more sunlight will grow taller than plants that receive less sunlight.

Research Question: Do students who eat breakfast perform better in school exams than those who don’t? Hypothesis: Students who eat a morning breakfast will score higher on school exams compared to students who skip breakfast.

Research Question: Does listening to music while studying impact a student’s ability to retain information? Hypothesis 1 (Directional): Students who listen to music while studying will retain less information than those who study in silence. Hypothesis 2 (Non-directional): There will be a difference in information retention between students who listen to music while studying and those who study in silence.

How can ResearchProspect Help?

If you are unsure about how to rest a research hypothesis in a dissertation or simply unsure about how to develop a hypothesis for your research, then you can take advantage of our dissertation services which cover every tiny aspect of a dissertation project you might need help with including but not limited to setting up a hypothesis and research questions,  help with individual chapters ,  full dissertation writing ,  statistical analysis , and much more.

Frequently Asked Questions

What are the 5 rules for writing a good hypothesis.

• Clear Statement: State a clear relationship between variables.
• Testable: Ensure it can be investigated and measured.
• Specific: Avoid vague terms, be precise in predictions.
• Falsifiable: Design to allow potential disproof.
• Relevant: Address research question and align with existing knowledge.

What is a hypothesis in simple words?

A hypothesis is an educated guess or prediction about something that can be tested. It is a statement that suggests a possible explanation for an event or phenomenon based on prior knowledge or observation. Scientists use hypotheses as a starting point for experiments to discover if they are true or false.

What is the hypothesis and examples?

A hypothesis is a testable prediction or explanation for an observation or phenomenon. For example, if plants are given sunlight, then they will grow. In this case, the hypothesis suggests that sunlight has a positive effect on plant growth. It can be tested by experimenting with plants in varying light conditions.

What is the hypothesis in research definition?

A hypothesis in research is a clear, testable statement predicting the possible outcome of a study based on prior knowledge and observation. It serves as the foundation for conducting experiments or investigations. Researchers test the validity of the hypothesis to draw conclusions and advance knowledge in a particular field.

Why is it called a hypothesis?

The term “hypothesis” originates from the Greek word “hypothesis,” which means “base” or “foundation.” It’s used to describe a foundational statement or proposition that can be tested. In scientific contexts, it denotes a tentative explanation for a phenomenon, serving as a starting point for investigation or experimentation.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

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).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

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 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).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Step-by-Step Guide: How to Craft a Strong Research Hypothesis

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Table of Contents

A research hypothesis is a concise statement about the expected result of an experiment or project. In many ways, a research hypothesis represents the starting point for a scientific endeavor, as it establishes a tentative assumption that is eventually substantiated or falsified, ultimately improving our certainty about the subject investigated.   

To help you with this and ease the process, in this article, we discuss the purpose of research hypotheses and list the most essential qualities of a compelling hypothesis. Let’s find out!  

How to Craft a Research Hypothesis  

Crafting a research hypothesis begins with a comprehensive literature review to identify a knowledge gap in your field. Once you find a question or problem, come up with a possible answer or explanation, which becomes your hypothesis. Now think about the specific methods of experimentation that can prove or disprove the hypothesis, which ultimately lead to the results of the study.   

Enlisted below are some standard formats in which you can formulate a hypothesis¹ :  

• A hypothesis can use the if/then format when it seeks to explore the correlation between two variables in a study primarily.  

Example: If administered drug X, then patients will experience reduced fatigue from cancer treatment.  

• A hypothesis can adopt when X/then Y format when it primarily aims to expose a connection between two variables  

Example: When workers spend a significant portion of their waking hours in sedentary work , then they experience a greater frequency of digestive problems.  

• A hypothesis can also take the form of a direct statement.  

Example: Drug X and drug Y reduce the risk of cognitive decline through the same chemical pathways  

What are the Features of an Effective Hypothesis?  

Hypotheses in research need to satisfy specific criteria to be considered scientifically rigorous. Here are the most notable qualities of a strong hypothesis:  

• Testability: Ensure the hypothesis allows you to work towards observable and testable results.  
• Brevity and objectivity: Present your hypothesis as a brief statement and avoid wordiness.  
• Clarity and Relevance: The hypothesis should reflect a clear idea of what we know and what we expect to find out about a phenomenon and address the significant knowledge gap relevant to a field of study.   

Understanding Null and Alternative Hypotheses in Research  

There are two types of hypotheses used commonly in research that aid statistical analyses. These are known as the null hypothesis and the alternative hypothesis . A null hypothesis is a statement assumed to be factual in the initial phase of the study.   

For example, if a researcher is testing the efficacy of a new drug, then the null hypothesis will posit that the drug has no benefits compared to an inactive control or placebo . Suppose the data collected through a drug trial leads a researcher to reject the null hypothesis. In that case, it is considered to substantiate the alternative hypothesis in the above example, that the new drug provides benefits compared to the placebo.  

Let’s take a closer look at the null hypothesis and alternative hypothesis with two more examples:  

Null Hypothesis:  

The rate of decline in the number of species in habitat X in the last year is the same as in the last 100 years when controlled for all factors except the recent wildfires.  

In the next experiment, the researcher will experimentally reject this null hypothesis in order to confirm the following alternative hypothesis :  

The rate of decline in the number of species in habitat X in the last year is different from the rate of decline in the last 100 years when controlled for all factors other than the recent wildfires.  

In the pair of null and alternative hypotheses stated above, a statistical comparison of the rate of species decline over a century and the preceding year will help the research experimentally test the null hypothesis, helping to draw scientifically valid conclusions about two factors—wildfires and species decline.   

We also recommend that researchers pay attention to contextual echoes and connections when writing research hypotheses. Research hypotheses are often closely linked to the introduction ² , such as the context of the study, and can similarly influence the reader’s judgment of the relevance and validity of the research hypothesis.  

Seasoned experts, such as professionals at Elsevier Language Services, guide authors on how to best embed a hypothesis within an article so that it communicates relevance and credibility. Contact us if you want help in ensuring readers find your hypothesis robust and unbiased.  

References  

• Hypotheses – The University Writing Center. (n.d.). https://writingcenter.tamu.edu/writing-speaking-guides/hypotheses  
• Shaping the research question and hypothesis. (n.d.). Students. https://students.unimelb.edu.au/academic-skills/graduate-research-services/writing-thesis-sections-part-2/shaping-the-research-question-and-hypothesis  

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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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How to Write a Research Hypothesis

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Since grade school, we've all been familiar with hypotheses. The hypothesis is an essential step of the scientific method. But what makes an effective research hypothesis, how do you create one, and what types of hypotheses are there? We answer these questions and more.

Updated on April 27, 2022

What is a research hypothesis?

General hypothesis.

Since grade school, we've all been familiar with the term “hypothesis.” A hypothesis is a fact-based guess or prediction that has not been proven. It is an essential step of the scientific method. The hypothesis of a study is a drive for experimentation to either prove the hypothesis or dispute it.

Research Hypothesis

A research hypothesis is more specific than a general hypothesis. It is an educated, expected prediction of the outcome of a study that is testable.

What makes an effective research hypothesis?

A good research hypothesis is a clear statement of the relationship between a dependent variable(s) and independent variable(s) relevant to the study that can be disproven.

Research hypothesis checklist

Once you've written a possible hypothesis, make sure it checks the following boxes:

• It must be testable: You need a means to prove your hypothesis. If you can't test it, it's not a hypothesis.
• It must include a dependent and independent variable: At least one independent variable ( cause ) and one dependent variable ( effect ) must be included.
• The language must be easy to understand: Be as clear and concise as possible. Nothing should be left to interpretation.
• It must be relevant to your research topic: You probably shouldn't be talking about cats and dogs if your research topic is outer space. Stay relevant to your topic.

How to create an effective research hypothesis

Pose it as a question first.

Start your research hypothesis from a journalistic approach. Ask one of the five W's: Who, what, when, where, or why.

A possible initial question could be: Why is the sky blue?

Do the preliminary research

Once you have a question in mind, read research around your topic. Collect research from academic journals.

If you're looking for information about the sky and why it is blue, research information about the atmosphere, weather, space, the sun, etc.

Write a draft hypothesis

Once you're comfortable with your subject and have preliminary knowledge, create a working hypothesis. Don't stress much over this. Your first hypothesis is not permanent. Look at it as a draft.

Your first draft of a hypothesis could be: Certain molecules in the Earth's atmosphere are responsive to the sky being the color blue.

Make your working draft perfect

Take your working hypothesis and make it perfect. Narrow it down to include only the information listed in the “Research hypothesis checklist” above.

Now that you've written your working hypothesis, narrow it down. Your new hypothesis could be: Light from the sun hitting oxygen molecules in the sky makes the color of the sky appear blue.

Write a null hypothesis

Your null hypothesis should be the opposite of your research hypothesis. It should be able to be disproven by your research.

In this example, your null hypothesis would be: Light from the sun hitting oxygen molecules in the sky does not make the color of the sky appear blue.

Why is it important to have a clear, testable hypothesis?

One of the main reasons a manuscript can be rejected from a journal is because of a weak hypothesis. “Poor hypothesis, study design, methodology, and improper use of statistics are other reasons for rejection of a manuscript,” says Dr. Ish Kumar Dhammi and Dr. Rehan-Ul-Haq in Indian Journal of Orthopaedics.

According to Dr. James M. Provenzale in American Journal of Roentgenology , “The clear declaration of a research question (or hypothesis) in the Introduction is critical for reviewers to understand the intent of the research study. It is best to clearly state the study goal in plain language (for example, “We set out to determine whether condition x produces condition y.”) An insufficient problem statement is one of the more common reasons for manuscript rejection.”

Characteristics that make a hypothesis weak include:

• Unclear variables
• Unoriginality
• Too general
• Too specific

A weak hypothesis leads to weak research and methods . The goal of a paper is to prove or disprove a hypothesis - or to prove or disprove a null hypothesis. If the hypothesis is not a dependent variable of what is being studied, the paper's methods should come into question.

A strong hypothesis is essential to the scientific method. A hypothesis states an assumed relationship between at least two variables and the experiment then proves or disproves that relationship with statistical significance. Without a proven and reproducible relationship, the paper feeds into the reproducibility crisis. Learn more about writing for reproducibility .

In a study published in The Journal of Obstetrics and Gynecology of India by Dr. Suvarna Satish Khadilkar, she reviewed 400 rejected manuscripts to see why they were rejected. Her studies revealed that poor methodology was a top reason for the submission having a final disposition of rejection.

Aside from publication chances, Dr. Gareth Dyke believes a clear hypothesis helps efficiency.

“Developing a clear and testable hypothesis for your research project means that you will not waste time, energy, and money with your work,” said Dyke. “Refining a hypothesis that is both meaningful, interesting, attainable, and testable is the goal of all effective research.”

Types of research hypotheses

There can be overlap in these types of hypotheses.

Simple hypothesis

A simple hypothesis is a hypothesis at its most basic form. It shows the relationship of one independent and one independent variable.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable).

Complex hypothesis

A complex hypothesis shows the relationship of two or more independent and dependent variables.

Example: Drinking soda (independent variable) every day leads to obesity (dependent variable) and heart disease (dependent variable).

Directional hypothesis

A directional hypothesis guesses which way the results of an experiment will go. It uses words like increase, decrease, higher, lower, positive, negative, more, or less. It is also frequently used in statistics.

Example: Humans exposed to radiation have a higher risk of cancer than humans not exposed to radiation.

Non-directional hypothesis

A non-directional hypothesis says there will be an effect on the dependent variable, but it does not say which direction.

Associative hypothesis

An associative hypothesis says that when one variable changes, so does the other variable.

Alternative hypothesis

An alternative hypothesis states that the variables have a relationship.

• The opposite of a null hypothesis

Example: An apple a day keeps the doctor away.

Null hypothesis

A null hypothesis states that there is no relationship between the two variables. It is posed as the opposite of what the alternative hypothesis states.

Researchers use a null hypothesis to work to be able to reject it. A null hypothesis:

• Can never be proven
• Can only be rejected
• Is the opposite of an alternative hypothesis

Example: An apple a day does not keep the doctor away.

Logical hypothesis

A logical hypothesis is a suggested explanation while using limited evidence.

Example: Bats can navigate in the dark better than tigers.

In this hypothesis, the researcher knows that tigers cannot see in the dark, and bats mostly live in darkness.

Empirical hypothesis

An empirical hypothesis is also called a “working hypothesis.” It uses the trial and error method and changes around the independent variables.

• An apple a day keeps the doctor away.
• Two apples a day keep the doctor away.
• Three apples a day keep the doctor away.

In this case, the research changes the hypothesis as the researcher learns more about his/her research.

Statistical hypothesis

A statistical hypothesis is a look of a part of a population or statistical model. This type of hypothesis is especially useful if you are making a statement about a large population. Instead of having to test the entire population of Illinois, you could just use a smaller sample of people who live there.

Example: 70% of people who live in Illinois are iron deficient.

Causal hypothesis

A causal hypothesis states that the independent variable will have an effect on the dependent variable.

Example: Using tobacco products causes cancer.

Final thoughts

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How to write a research hypothesis

Last updated

19 January 2023

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Miroslav Damyanov

Start with a broad subject matter that excites you, so your curiosity will motivate your work. Conduct a literature search to determine the range of questions already addressed and spot any holes in the existing research.

Narrow the topics that interest you and determine your research question. Rather than focusing on a hole in the research, you might choose to challenge an existing assumption, a process called problematization. You may also find yourself with a short list of questions or related topics.

Use the FINER method to determine the single problem you'll address with your research. FINER stands for:

I nteresting

You need a feasible research question, meaning that there is a way to address the question. You should find it interesting, but so should a larger audience. Rather than repeating research that others have already conducted, your research hypothesis should test something novel or unique. 

The research must fall into accepted ethical parameters as defined by the government of your country and your university or college if you're an academic. You'll also need to come up with a relevant question since your research should provide a contribution to the existing research area.

This process typically narrows your shortlist down to a single problem you'd like to study and the variable you want to test. You're ready to write your hypothesis statements.

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• Types of research hypotheses

It is important to narrow your topic down to one idea before trying to write your research hypothesis. You'll only test one problem at a time. To do this, you'll write two hypotheses – a null hypothesis (H0) and an alternative hypothesis (Ha).

You'll come across many terms related to developing a research hypothesis or referring to a specific type of hypothesis. Let's take a quick look at these terms.

Null hypothesis

The term null hypothesis refers to a research hypothesis type that assumes no statistically significant relationship exists within a set of observations or data. It represents a claim that assumes that any observed relationship is due to chance. Represented as H0, the null represents the conjecture of the research.

Alternative hypothesis

The alternative hypothesis accompanies the null hypothesis. It states that the situation presented in the null hypothesis is false or untrue, and claims an observed effect in your test. This is typically denoted by Ha or H(n), where “n” stands for the number of alternative hypotheses. You can have more than one alternative hypothesis. 

Simple hypothesis

The term simple hypothesis refers to a hypothesis or theory that predicts the relationship between two variables - the independent (predictor) and the dependent (predicted). 

Complex hypothesis

The term complex hypothesis refers to a model – either quantitative (mathematical) or qualitative . A complex hypothesis states the surmised relationship between two or more potentially related variables.

Directional hypothesis

When creating a statistical hypothesis, the directional hypothesis (the null hypothesis) states an assumption regarding one parameter of a population. Some academics call this the “one-sided” hypothesis. The alternative hypothesis indicates whether the researcher tests for a positive or negative effect by including either the greater than (">") or less than ("<") sign.

Non-directional hypothesis

We refer to the alternative hypothesis in a statistical research question as a non-directional hypothesis. It includes the not equal ("≠") sign to show that the research tests whether or not an effect exists without specifying the effect's direction (positive or negative).

Associative hypothesis

The term associative hypothesis assumes a link between two variables but stops short of stating that one variable impacts the other. Academic statistical literature asserts in this sense that correlation does not imply causation. So, although the hypothesis notes the correlation between two variables – the independent and dependent - it does not predict how the two interact.

Logical hypothesis

Typically used in philosophy rather than science, researchers can't test a logical hypothesis because the technology or data set doesn't yet exist. A logical hypothesis uses logic as the basis of its assumptions. 

In some cases, a logical hypothesis can become an empirical hypothesis once technology provides an opportunity for testing. Until that time, the question remains too expensive or complex to address. Note that a logical hypothesis is not a statistical hypothesis.

Empirical hypothesis

When we consider the opposite of a logical hypothesis, we call this an empirical or working hypothesis. This type of hypothesis considers a scientifically measurable question. A researcher can consider and test an empirical hypothesis through replicable tests, observations, and measurements.

Statistical hypothesis

The term statistical hypothesis refers to a test of a theory that uses representative statistical models to test relationships between variables to draw conclusions regarding a large population. This requires an existing large data set, commonly referred to as big data, or implementing a survey to obtain original statistical information to form a data set for the study. 

Testing this type of hypothesis requires the use of random samples. Note that the null and alternative hypotheses are used in statistical hypothesis testing.

Causal hypothesis

The term causal hypothesis refers to a research hypothesis that tests a cause-and-effect relationship. A causal hypothesis is utilized when conducting experimental or quasi-experimental research.

Descriptive hypothesis

The term descriptive hypothesis refers to a research hypothesis used in non-experimental research, specifying an influence in the relationship between two variables.

• What makes an effective research hypothesis?

An effective research hypothesis offers a clearly defined, specific statement, using simple wording that contains no assumptions or generalizations, and that you can test. A well-written hypothesis should predict the tested relationship and its outcome. It contains zero ambiguity and offers results you can observe and test. 

The research hypothesis should address a question relevant to a research area. Overall, your research hypothesis needs the following essentials:

Hypothesis Essential #1: Specificity & Clarity

Hypothesis Essential #2: Testability (Provability)

• How to develop a good research hypothesis

In developing your hypothesis statements, you must pre-plan some of your statistical analysis. Once you decide on your problem to examine, determine three aspects:

the parameter you'll test

the test's direction (left-tailed, right-tailed, or non-directional)

the hypothesized parameter value

Any quantitative research includes a hypothesized parameter value of a mean, a proportion, or the difference between two proportions. Here's how to note each parameter:

Single mean (μ)

Paired means (μd)

Single proportion (p)

Difference between two independent means (μ1−μ2)

Difference between two proportions (p1−p2)

Simple linear regression slope (β)

Correlation (ρ)

Defining these parameters and determining whether you want to test the mean, proportion, or differences helps you determine the statistical tests you'll conduct to analyze your data. When writing your hypothesis, you only need to decide which parameter to test and in what overarching way.

The null research hypothesis must include everyday language, in a single sentence, stating the problem you want to solve. Write it as an if-then statement with defined variables. Write an alternative research hypothesis that states the opposite.

• What is the correct format for writing a hypothesis?

The following example shows the proper format and textual content of a hypothesis. It follows commonly accepted academic standards.

Null hypothesis (H0): High school students who participate in varsity sports as opposed to those who do not, fail to score higher on leadership tests than students who do not participate.

Alternative hypothesis (H1): High school students who play a varsity sport as opposed to those who do not participate in team athletics will score higher on leadership tests than students who do not participate in athletics.

The research question tests the correlation between varsity sports participation and leadership qualities expressed as a score on leadership tests. It compares the population of athletes to non-athletes.

• What are the five steps of a hypothesis?

Once you decide on the specific problem or question you want to address, you can write your research hypothesis. Use this five-step system to hone your null hypothesis and generate your alternative hypothesis.

Step 1 : Create your research question. This topic should interest and excite you; answering it provides relevant information to an industry or academic area.

Step 2 : Conduct a literature review to gather essential existing research.

Step 3 : Write a clear, strong, simply worded sentence that explains your test parameter, test direction, and hypothesized parameter.

Step 4 : Read it a few times. Have others read it and ask them what they think it means. Refine your statement accordingly until it becomes understandable to everyone. While not everyone can or will comprehend every research study conducted, any person from the general population should be able to read your hypothesis and alternative hypothesis and understand the essential question you want to answer.

Step 5 : Re-write your null hypothesis until it reads simply and understandably. Write your alternative hypothesis.

What is the Red Queen hypothesis?

Some hypotheses are well-known, such as the Red Queen hypothesis. Choose your wording carefully, since you could become like the famed scientist Dr. Leigh Van Valen. In 1973, Dr. Van Valen proposed the Red Queen hypothesis to describe coevolutionary activity, specifically reciprocal evolutionary effects between species to explain extinction rates in the fossil record. 

Essentially, Van Valen theorized that to survive, each species remains in a constant state of adaptation, evolution, and proliferation, and constantly competes for survival alongside other species doing the same. Only by doing this can a species avoid extinction. Van Valen took the hypothesis title from the Lewis Carroll book, "Through the Looking Glass," which contains a key character named the Red Queen who explains to Alice that for all of her running, she's merely running in place.

• Getting started with your research

In conclusion, once you write your null hypothesis (H0) and an alternative hypothesis (Ha), you’ve essentially authored the elevator pitch of your research. These two one-sentence statements describe your topic in simple, understandable terms that both professionals and laymen can understand. They provide the starting point of your research project.

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15 Hypothesis Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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A hypothesis is defined as a testable prediction , and is used primarily in scientific experiments as a potential or predicted outcome that scientists attempt to prove or disprove (Atkinson et al., 2021; Tan, 2022).

In my types of hypothesis article, I outlined 13 different hypotheses, including the directional hypothesis (which makes a prediction about an effect of a treatment will be positive or negative) and the associative hypothesis (which makes a prediction about the association between two variables).

This article will dive into some interesting examples of hypotheses and examine potential ways you might test each one.

Hypothesis Examples

1. “inadequate sleep decreases memory retention”.

Field: Psychology

Type: Causal Hypothesis A causal hypothesis explores the effect of one variable on another. This example posits that a lack of adequate sleep causes decreased memory retention. In other words, if you are not getting enough sleep, your ability to remember and recall information may suffer.

How to Test:

To test this hypothesis, you might devise an experiment whereby your participants are divided into two groups: one receives an average of 8 hours of sleep per night for a week, while the other gets less than the recommended sleep amount.

During this time, all participants would daily study and recall new, specific information. You’d then measure memory retention of this information for both groups using standard memory tests and compare the results.

Should the group with less sleep have statistically significant poorer memory scores, the hypothesis would be supported.

Ensuring the integrity of the experiment requires taking into account factors such as individual health differences, stress levels, and daily nutrition.

Relevant Study: Sleep loss, learning capacity and academic performance (Curcio, Ferrara & De Gennaro, 2006)

2. “Increase in Temperature Leads to Increase in Kinetic Energy”

Field: Physics

Type: Deductive Hypothesis The deductive hypothesis applies the logic of deductive reasoning – it moves from a general premise to a more specific conclusion. This specific hypothesis assumes that as temperature increases, the kinetic energy of particles also increases – that is, when you heat something up, its particles move around more rapidly.

This hypothesis could be examined by heating a gas in a controlled environment and capturing the movement of its particles as a function of temperature.

You’d gradually increase the temperature and measure the kinetic energy of the gas particles with each increment. If the kinetic energy consistently rises with the temperature, your hypothesis gets supporting evidence.

Variables such as pressure and volume of the gas would need to be held constant to ensure validity of results.

3. “Children Raised in Bilingual Homes Develop Better Cognitive Skills”

Field: Psychology/Linguistics

Type: Comparative Hypothesis The comparative hypothesis posits a difference between two or more groups based on certain variables. In this context, you might propose that children raised in bilingual homes have superior cognitive skills compared to those raised in monolingual homes.

Testing this hypothesis could involve identifying two groups of children: those raised in bilingual homes, and those raised in monolingual homes.

Cognitive skills in both groups would be evaluated using a standard cognitive ability test at different stages of development. The examination would be repeated over a significant time period for consistency.

If the group raised in bilingual homes persistently scores higher than the other, the hypothesis would thereby be supported.

The challenge for the researcher would be controlling for other variables that could impact cognitive development, such as socio-economic status, education level of parents, and parenting styles.

Relevant Study: The cognitive benefits of being bilingual (Marian & Shook, 2012)

4. “High-Fiber Diet Leads to Lower Incidences of Cardiovascular Diseases”

Field: Medicine/Nutrition

Type: Alternative Hypothesis The alternative hypothesis suggests an alternative to a null hypothesis. In this context, the implied null hypothesis could be that diet has no effect on cardiovascular health, which the alternative hypothesis contradicts by suggesting that a high-fiber diet leads to fewer instances of cardiovascular diseases.

To test this hypothesis, a longitudinal study could be conducted on two groups of participants; one adheres to a high-fiber diet, while the other follows a diet low in fiber.

After a fixed period, the cardiovascular health of participants in both groups could be analyzed and compared. If the group following a high-fiber diet has a lower number of recorded cases of cardiovascular diseases, it would provide evidence supporting the hypothesis.

Control measures should be implemented to exclude the influence of other lifestyle and genetic factors that contribute to cardiovascular health.

Relevant Study: Dietary fiber, inflammation, and cardiovascular disease (King, 2005)

5. “Gravity Influences the Directional Growth of Plants”

Field: Agronomy / Botany

Type: Explanatory Hypothesis An explanatory hypothesis attempts to explain a phenomenon. In this case, the hypothesis proposes that gravity affects how plants direct their growth – both above-ground (toward sunlight) and below-ground (towards water and other resources).

The testing could be conducted by growing plants in a rotating cylinder to create artificial gravity.

Observations on the direction of growth, over a specified period, can provide insights into the influencing factors. If plants consistently direct their growth in a manner that indicates the influence of gravitational pull, the hypothesis is substantiated.

It is crucial to ensure that other growth-influencing factors, such as light and water, are uniformly distributed so that only gravity influences the directional growth.

6. “The Implementation of Gamified Learning Improves Students’ Motivation”

Field: Education

Type: Relational Hypothesis The relational hypothesis describes the relation between two variables. Here, the hypothesis is that the implementation of gamified learning has a positive effect on the motivation of students.

To validate this proposition, two sets of classes could be compared: one that implements a learning approach with game-based elements, and another that follows a traditional learning approach.

The students’ motivation levels could be gauged by monitoring their engagement, performance, and feedback over a considerable timeframe.

If the students engaged in the gamified learning context present higher levels of motivation and achievement, the hypothesis would be supported.

Control measures ought to be put into place to account for individual differences, including prior knowledge and attitudes towards learning.

Relevant Study: Does educational gamification improve students’ motivation? (Chapman & Rich, 2018)

7. “Mathematics Anxiety Negatively Affects Performance”

Field: Educational Psychology

Type: Research Hypothesis The research hypothesis involves making a prediction that will be tested. In this case, the hypothesis proposes that a student’s anxiety about math can negatively influence their performance in math-related tasks.

To assess this hypothesis, researchers must first measure the mathematics anxiety levels of a sample of students using a validated instrument, such as the Mathematics Anxiety Rating Scale.

Then, the students’ performance in mathematics would be evaluated through standard testing. If there’s a negative correlation between the levels of math anxiety and math performance (meaning as anxiety increases, performance decreases), the hypothesis would be supported.

It would be crucial to control for relevant factors such as overall academic performance and previous mathematical achievement.

8. “Disruption of Natural Sleep Cycle Impairs Worker Productivity”

Field: Organizational Psychology

Type: Operational Hypothesis The operational hypothesis involves defining the variables in measurable terms. In this example, the hypothesis posits that disrupting the natural sleep cycle, for instance through shift work or irregular working hours, can lessen productivity among workers.

To test this hypothesis, you could collect data from workers who maintain regular working hours and those with irregular schedules.

Measuring productivity could involve examining the worker’s ability to complete tasks, the quality of their work, and their efficiency.

If workers with interrupted sleep cycles demonstrate lower productivity compared to those with regular sleep patterns, it would lend support to the hypothesis.

Consideration should be given to potential confounding variables such as job type, worker age, and overall health.

9. “Regular Physical Activity Reduces the Risk of Depression”

Field: Health Psychology

Type: Predictive Hypothesis A predictive hypothesis involves making a prediction about the outcome of a study based on the observed relationship between variables. In this case, it is hypothesized that individuals who engage in regular physical activity are less likely to suffer from depression.

Longitudinal studies would suit to test this hypothesis, tracking participants’ levels of physical activity and their mental health status over time.

The level of physical activity could be self-reported or monitored, while mental health status could be assessed using standard diagnostic tools or surveys.

If data analysis shows that participants maintaining regular physical activity have a lower incidence of depression, this would endorse the hypothesis.

However, care should be taken to control other lifestyle and behavioral factors that could intervene with the results.

Relevant Study: Regular physical exercise and its association with depression (Kim, 2022)

10. “Regular Meditation Enhances Emotional Stability”

Type: Empirical Hypothesis In the empirical hypothesis, predictions are based on amassed empirical evidence . This particular hypothesis theorizes that frequent meditation leads to improved emotional stability, resonating with numerous studies linking meditation to a variety of psychological benefits.

Earlier studies reported some correlations, but to test this hypothesis directly, you’d organize an experiment where one group meditates regularly over a set period while a control group doesn’t.

Both groups’ emotional stability levels would be measured at the start and end of the experiment using a validated emotional stability assessment.

If regular meditators display noticeable improvements in emotional stability compared to the control group, the hypothesis gains credit.

You’d have to ensure a similar emotional baseline for all participants at the start to avoid skewed results.

11. “Children Exposed to Reading at an Early Age Show Superior Academic Progress”

Type: Directional Hypothesis The directional hypothesis predicts the direction of an expected relationship between variables. Here, the hypothesis anticipates that early exposure to reading positively affects a child’s academic advancement.

A longitudinal study tracking children’s reading habits from an early age and their consequent academic performance could validate this hypothesis.

Parents could report their children’s exposure to reading at home, while standardized school exam results would provide a measure of academic achievement.

If the children exposed to early reading consistently perform better acadically, it gives weight to the hypothesis.

However, it would be important to control for variables that might impact academic performance, such as socioeconomic background, parental education level, and school quality.

12. “Adopting Energy-efficient Technologies Reduces Carbon Footprint of Industries”

Field: Environmental Science

Type: Descriptive Hypothesis A descriptive hypothesis predicts the existence of an association or pattern related to variables. In this scenario, the hypothesis suggests that industries adopting energy-efficient technologies will resultantly show a reduced carbon footprint.

Global industries making use of energy-efficient technologies could track their carbon emissions over time. At the same time, others not implementing such technologies continue their regular tracking.

After a defined time, the carbon emission data of both groups could be compared. If industries that adopted energy-efficient technologies demonstrate a notable reduction in their carbon footprints, the hypothesis would hold strong.

In the experiment, you would exclude variations brought by factors such as industry type, size, and location.

13. “Reduced Screen Time Improves Sleep Quality”

Type: Simple Hypothesis The simple hypothesis is a prediction about the relationship between two variables, excluding any other variables from consideration. This example posits that by reducing time spent on devices like smartphones and computers, an individual should experience improved sleep quality.

A sample group would need to reduce their daily screen time for a pre-determined period. Sleep quality before and after the reduction could be measured using self-report sleep diaries and objective measures like actigraphy, monitoring movement and wakefulness during sleep.

If the data shows that sleep quality improved post the screen time reduction, the hypothesis would be validated.

Other aspects affecting sleep quality, like caffeine intake, should be controlled during the experiment.

Relevant Study: Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep (Waller et al., 2021)

14. Engaging in Brain-Training Games Improves Cognitive Functioning in Elderly

Field: Gerontology

Type: Inductive Hypothesis Inductive hypotheses are based on observations leading to broader generalizations and theories. In this context, the hypothesis deduces from observed instances that engaging in brain-training games can help improve cognitive functioning in the elderly.

A longitudinal study could be conducted where an experimental group of elderly people partakes in regular brain-training games.

Their cognitive functioning could be assessed at the start of the study and at regular intervals using standard neuropsychological tests.

If the group engaging in brain-training games shows better cognitive functioning scores over time compared to a control group not playing these games, the hypothesis would be supported.

15. Farming Practices Influence Soil Erosion Rates

Type: Null Hypothesis A null hypothesis is a negative statement assuming no relationship or difference between variables. The hypothesis in this context asserts there’s no effect of different farming practices on the rates of soil erosion.

Comparing soil erosion rates in areas with different farming practices over a considerable timeframe could help test this hypothesis.

If, statistically, the farming practices do not lead to differences in soil erosion rates, the null hypothesis is accepted.

However, if marked variation appears, the null hypothesis is rejected, meaning farming practices do influence soil erosion rates. It would be crucial to control for external factors like weather, soil type, and natural vegetation.

The variety of hypotheses mentioned above underscores the diversity of research constructs inherent in different fields, each with its unique purpose and way of testing.

While researchers may develop hypotheses primarily as tools to define and narrow the focus of the study, these hypotheses also serve as valuable guiding forces for the data collection and analysis procedures, making the research process more efficient and direction-focused.

Hypotheses serve as a compass for any form of academic research. The diverse examples provided, from Psychology to Educational Studies, Environmental Science to Gerontology, clearly demonstrate how certain hypotheses suit specific fields more aptly than others.

It is important to underline that although these varied hypotheses differ in their structure and methods of testing, each endorses the fundamental value of empiricism in research. Evidence-based decision making remains at the heart of scholarly inquiry, regardless of the research field, thus aligning all hypotheses to the core purpose of scientific investigation.

Testing hypotheses is an essential part of the scientific method . By doing so, researchers can either confirm their predictions, giving further validity to an existing theory, or they might uncover new insights that could potentially shift the field’s understanding of a particular phenomenon. In either case, hypotheses serve as the stepping stones for scientific exploration and discovery.

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (2021).  SAGE research methods foundations . SAGE Publications Ltd.

Curcio, G., Ferrara, M., & De Gennaro, L. (2006). Sleep loss, learning capacity and academic performance.  Sleep medicine reviews ,  10 (5), 323-337.

Kim, J. H. (2022). Regular physical exercise and its association with depression: A population-based study short title: Exercise and depression.  Psychiatry Research ,  309 , 114406.

King, D. E. (2005). Dietary fiber, inflammation, and cardiovascular disease.  Molecular nutrition & food research ,  49 (6), 594-600.

Marian, V., & Shook, A. (2012, September). The cognitive benefits of being bilingual. In Cerebrum: the Dana forum on brain science (Vol. 2012). Dana Foundation.

Tan, W. C. K. (2022). Research Methods: A Practical Guide For Students And Researchers (Second Edition) . World Scientific Publishing Company.

Waller, N. A., Zhang, N., Cocci, A. H., D’Agostino, C., Wesolek‐Greenson, S., Wheelock, K., … & Resnicow, K. (2021). Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep. Child: care, health and development, 47 (5), 618-626.

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Each academic research revolves around specific statement or problem — a research hypothesis. 

A hypothesis is a suggested prediction for a phenomenon or observed event, based on prior knowledge or research. It is a tentative statement that can be tested through further investigation and analysis. A hypothesis usually takes the form of a statement that suggests a relationship between two or more variables.

Every research project, be it a a term paper, research paper or a dissertation, should begin with defining a hypothesis. While this may seem simple, in reality beginners face a lot of problems. This includes difficulty with formulating a hypothesis accurately and capturing the main idea. In this blog post, we will tell you how to write a hypothesis so it is accurate and correct.

What Is a Research Hypothesis: Expanded Definition

A research hypothesis is a statement or assumption that answers a question you asked earlier but haven't tested yet. In fact, this is basis of your work which you use to prove or reject your assumption. Major research projects most often deal with several hypotheses. These relate to various aspects of an issue under study. Thus, you will divide assumptions by research sectors and study them in a segmented manner. When making an assignment, one must work based on an existing theory and gained knowledge. One must also take into account that it must be testable. That is, it can be rejected or confirmed with methods of scientific research. Hypothesis example may look like this:

In your work, you must prove or reject this hypothesis by providing survey results. Show some statistical analysis , study of reports and other processed data.

Remeber that you can hire a paper writer who will integrate survey outcomes and conduct statistical analysis in your research paper hypothesis. 

Variables in Hypotheses

To make a qualitative guess, you should consider variables in your hypothesis. They can be divided into independent and dependent ones. In fact, you must establish causal relationship between two or more variables. Independent ( confounding variable ) is what researcher can control or change, i.e. initial condition. Dependent ( extraneous variable ) is what researcher studies. It is observed in created conditions. Before you start learning how to write an assignment with independent and dependent variables, you should define the main idea of your work. For example, you take an assumption that eating hedgehog meat reduces risk of cardiovascular disease. Independent variable is hedgehog meat consumption, which is cause. Improvement in cardiovascular health is a dependent variable – an intended effect.

How to Write a Hypothesis: 5 Simple Writing Steps

Novice researchers most frequently ask how to write a hypothesis statement. This is a complex process that includes compilation of laconic predictions. These are based on conducted experiments. We can support you in this task. We have developed 5 steps for researchers so they can write a high-quality and comprehensive assignment.

Step 1. Generate a Question Before Writing Your Hypothesis

At the first stage of writing a hypothesis for a research paper you must define a research question that you need to answer. It should be focused on particular problem. Try to make it specific and yet suitable for research within framework of your project. To write quality assignment, you must use 6 classic statements. Thus, you must clarify: who, what, where, when, why and how. You must make question understandable in terms of positioning problem. Example of correct hypothesis:

Step 2. Gather Preliminary Research for Your Hypothesis

Before writing a research hypothesis, conduct some preliminary research to find out if your assumption is working and can be proved. You will get the key insights through observations or experiments. You can also use results of your colleagues who have already studied this issue. Thus, you will build a concept with formulated variables. You will study them and identify relationships between them.

Step 3. Write a Strong Hypothesis

With results of preliminary preparation and research questions, you can study how to write a strong hypothesis . First of all, highlight the main testing problem. You must formulate it as briefly as possible. Try to avoid stretching statements in an attempt to make paper longer. Be as clear as possible, avoid vague judgments. For example:

This is not good option. It is better to apply hypothesis in the form of:

This is a clear sentence that is devoid of unnecessary details. It allows you to immediately see an expected effect. Get practical help in writing research paper if you wish for more quality.

Step 4. Refine Your Research Hypothesis

Make sure a hypothesis for a research proposal formulated correctly. You must check if it has following elements:

• Dependent and independent variables.
• An object or phenomenon for testing.
• Expected outcome of study that you plan to work through. This must be part of an experiment or an observation.

This way, you will specify question under study. You also will be able to verify it if needed. That is, you will move from general to particular.

Step 5. Write a Null Hypothesis

You may need to write a null hypothesis. Why and when, you may ask? When you use this method for processing specific statistics. You should specify if you plan to prove your point on its basis. In fact, it is clear position that doesn’t establish links between variables. For example, this statement is null hypothesis:

It is basis for presenting one's own opinion. It allows to build an evidence base stemming from researcher's evidence.

What Is the Difference Between a Null Hypothesis and an Alternative Hypothesis

To better understand how to write null and alternative hypothesis that will form backbone of study, examine testable statements. Based on results, null hypothesis is prepared. It is a statement with no connection between variables. At the same time, scientists usually work with an alternative hypothesis. Here, they have already found a connection between phenomena. Ever considered custom research paper writing service ? So, the above statement about frequency of doctor visits can be modified to research of: 

Hypothesis Examples

Quite often, researchers find it difficult to formulate basis for  writing a research paper . Therefore, some examples of hypothesis will be useful for them. This will correspond to if-then connections. With their help you will also briefly outline the main part of current research. We will help you in formulating an assignment and offer several working options:

Tips on Writing a Hypothesis

It’s difficult to start writing a hypothesis for a research proposal. Especially for aspiring academics! After all, it is important that an assignment is clear and specific. It must also be viable for further development. Here are some tips to help you formulate your statement:

• Analyze interesting aspects. Review current studies and problems on the selected topic. Highlight what you wanted to explore, perhaps it will be a concept close to your previous works.
• Clarify the details. Spend time on preliminary analysis. You must also highlight controversial aspects and contemporary issues. Sometimes, even well-researched phenomena can be promising.
• Focus on your own work. It’s always easier to continue than to start anew. At the same time, you might not have considered all the theses in the previous study.
• Make the variables clear. Avoid ambiguous statements.

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How to Write a Scientific Hypothesis: Final Thoughts

So, if you've come this far, you should already know how to how to write a hypothesis step by step. Before starting writing, analyze the problem and the topic. You should highlight the thesis that can be developed further. We recommend going through the following steps:

• Define the question you expect to receive an answer to.
• Do some preliminary research.
• Write it strongly.
• Refine it with variables, subject and phenomenon, and expected result.
• Make a null hypothesis and consider a different option.

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Frequently Asked Questions About Writing a Good Hypothesis

1. how can i improve my hypothesis.

To make the hypothesis working and of high quality, be sure you select both independent and dependent variables and add them to the statement. Examine the relationships of these elements. Think if you can prove them and explain them in further research.

2. Is there a maximum number of hypotheses that is allowed in one research paper?

You can write as many hypotheses as you want for your paper, because it all depends on your view on the topic and the desire to develop it in several directions. The main thing is that your project shouldn't be overloaded with too many hypotheses and that you pay enough attention to each of them.

3. How do I test my hypothesis?

It’s easy to test the statement before you write a hypothesis for a research proposal. Do an experiment: ask your question and try answering it. If you succeed, this assignment can be used for more detailed study.

4. How long is a hypothesis?

While writing the hypothesis, you must make it as direct as possible and, at the same time, clear it of extraneous judgments. Typically, it's 20 words long. We don’t recommend exceeding this volume, so as not to face difficulties in interpretation.

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How to Write a Hypothesis

If I [do something], then [this] will happen.

This basic statement/formula should be pretty familiar to all of you as it is the starting point of almost every scientific project or paper. It is a hypothesis – a statement that showcases what you “think” will happen during an experiment. This assumption is made based on the knowledge, facts, and data you already have.

How do you write a hypothesis? If you have a clear understanding of the proper structure of a hypothesis, you should not find it too hard to create one. However, if you have never written a hypothesis before, you might find it a bit frustrating. In this article from EssayPro - custom essay writing services , we are going to tell you everything you need to know about hypotheses, their types, and practical tips for writing them.

Hypothesis Definition

According to the definition, a hypothesis is an assumption one makes based on existing knowledge. To elaborate, it is a statement that translates the initial research question into a logical prediction shaped on the basis of available facts and evidence. To solve a specific problem, one first needs to identify the research problem (research question), conduct initial research, and set out to answer the given question by performing experiments and observing their outcomes. However, before one can move to the experimental part of the research, they should first identify what they expect to see for results. At this stage, a scientist makes an educated guess and writes a hypothesis that he or she is going to prove or refute in the course of their study.

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A hypothesis can also be seen as a form of development of knowledge. It is a well-grounded assumption put forward to clarify the properties and causes of the phenomena being studied.

As a rule, a hypothesis is formed based on a number of observations and examples that confirm it. This way, it looks plausible as it is backed up with some known information. The hypothesis is subsequently proved by turning it into an established fact or refuted (for example, by pointing out a counterexample), which allows it to attribute it to the category of false statements.

As a student, you may be asked to create a hypothesis statement as a part of your academic papers. Hypothesis-based approaches are commonly used among scientific academic works, including but not limited to research papers, theses, and dissertations.

Note that in some disciplines, a hypothesis statement is called a thesis statement. However, its essence and purpose remain unchanged – this statement aims to make an assumption regarding the outcomes of the investigation that will either be proved or refuted.

Characteristics and Sources of a Hypothesis

Now, as you know what a hypothesis is in a nutshell, let’s look at the key characteristics that define it:

• It has to be clear and accurate in order to look reliable.
• It has to be specific.
• There should be scope for further investigation and experiments.
• A hypothesis should be explained in simple language—while retaining its significance.
• If you are making a relational hypothesis, two essential elements you have to include are variables and the relationship between them.

The main sources of a hypothesis are:

• Scientific theories.
• Observations from previous studies and current experiences.
• The resemblance among different phenomena.
• General patterns that affect people’s thinking process.

Types of Hypothesis

Basically, there are two major types of scientific hypothesis: alternative and null.

• Alternative Hypothesis

This type of hypothesis is generally denoted as H1. This statement is used to identify the expected outcome of your research. According to the alternative hypothesis definition, this type of hypothesis can be further divided into two subcategories:

• Directional — a statement that explains the direction of the expected outcomes. Sometimes this type of hypothesis is used to study the relationship between variables rather than comparing between the groups.
• Non-directional — unlike the directional alternative hypothesis, a non-directional one does not imply a specific direction of the expected outcomes.

Now, let’s see an alternative hypothesis example for each type:

Directional: Attending more lectures will result in improved test scores among students. Non-directional: Lecture attendance will influence test scores among students.

Notice how in the directional hypothesis we specified that the attendance of more lectures will boost student’s performance on tests, whereas in the non-directional hypothesis we only stated that there is a relationship between the two variables (i.e. lecture attendance and students’ test scores) but did not specify whether the performance will improve or decrease.

• Null Hypothesis

This type of hypothesis is generally denoted as H0. This statement is the complete opposite of what you expect or predict will happen throughout the course of your study—meaning it is the opposite of your alternative hypothesis. Simply put, a null hypothesis claims that there is no exact or actual correlation between the variables defined in the hypothesis.

To give you a better idea of how to write a null hypothesis, here is a clear example: Lecture attendance has no effect on student’s test scores.

Both of these types of hypotheses provide specific clarifications and restatements of the research problem. The main difference between these hypotheses and a research problem is that the latter is just a question that can’t be tested, whereas hypotheses can.

Based on the alternative and null hypothesis examples provided earlier, we can conclude that the importance and main purpose of these hypotheses are that they deliver a rough description of the subject matter. The main purpose of these statements is to give an investigator a specific guess that can be directly tested in a study. Simply put, a hypothesis outlines the framework, scope, and direction for the study. Although null and alternative hypotheses are the major types, there are also a few more to keep in mind:

Research Hypothesis — a statement that is used to test the correlation between two or more variables.

For example: Eating vitamin-rich foods affects human health.

Simple Hypothesis — a statement used to indicate the correlation between one independent and one dependent variable.

For example: Eating more vegetables leads to better immunity.

Complex Hypothesis — a statement used to indicate the correlation between two or more independent variables and two or more dependent variables.

For example: Eating more fruits and vegetables leads to better immunity, weight loss, and lower risk of diseases.

Associative and Causal Hypothesis — an associative hypothesis is a statement used to indicate the correlation between variables under the scenario when a change in one variable inevitably changes the other variable. A causal hypothesis is a statement that highlights the cause and effect relationship between variables.

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Hypothesis vs Prediction

When speaking of hypotheses, another term that comes to mind is prediction. These two terms are often used interchangeably, which can be rather confusing. Although both a hypothesis and prediction can generally be defined as “guesses” and can be easy to confuse, these terms are different. The main difference between a hypothesis and a prediction is that the first is predominantly used in science, while the latter is most often used outside of science.

Simply put, a hypothesis is an intelligent assumption. It is a guess made regarding the nature of the unknown (or less known) phenomena based on existing knowledge, studies, and/or series of experiments, and is otherwise grounded by valid facts. The main purpose of a hypothesis is to use available facts to create a logical relationship between variables in order to provide a more precise scientific explanation. Additionally, hypotheses are statements that can be tested with further experiments. It is an assumption you make regarding the flow and outcome(s) of your research study.

A prediction, on the contrary, is a guess that often lacks grounding. Although, in theory, a prediction can be scientific, in most cases it is rather fictional—i.e. a pure guess that is not based on current knowledge and/or facts. As a rule, predictions are linked to foretelling events that may or may not occur in the future. Often, a person who makes predictions has little or no actual knowledge of the subject matter he or she makes the assumption about.

Another big difference between these terms is in the methodology used to prove each of them. A prediction can only be proven once. You can determine whether it is right or wrong only upon the occurrence or non-occurrence of the predicted event. A hypothesis, on the other hand, offers scope for further testing and experiments. Additionally, a hypothesis can be proven in multiple stages. This basically means that a single hypothesis can be proven or refuted numerous times by different scientists who use different scientific tools and methods.

To give you a better idea of how a hypothesis is different from a prediction, let’s look at the following examples:

Hypothesis: If I eat more vegetables and fruits, then I will lose weight faster.

This is a hypothesis because it is based on generally available knowledge (i.e. fruits and vegetables include fewer calories compared to other foods) and past experiences (i.e. people who give preference to healthier foods like fruits and vegetables are losing weight easier). It is still a guess, but it is based on facts and can be tested with an experiment.

Prediction: The end of the world will occur in 2023.

This is a prediction because it foretells future events. However, this assumption is fictional as it doesn’t have any actual grounded evidence supported by facts.

Based on everything that was said earlier and our examples, we can highlight the following key takeaways:

• A hypothesis, unlike a prediction, is a more intelligent assumption based on facts.
• Hypotheses define existing variables and analyze the relationship(s) between them.
• Predictions are most often fictional and lack grounding.
• A prediction is most often used to foretell events in the future.
• A prediction can only be proven once – when the predicted event occurs or doesn’t occur. 
• A hypothesis can remain a hypothesis even if one scientist has already proven or disproven it. Other scientists in the future can obtain a different result using other methods and tools.

We also recommend that you read about some informative essay topics .

Now, as you know what a hypothesis is, what types of it exist, and how it differs from a prediction, you are probably wondering how to state a hypothesis. In this section, we will guide you through the main stages of writing a good hypothesis and provide handy tips and examples to help you overcome this challenge:

1. Define Your Research Question

Here is one thing to keep in mind – regardless of the paper or project you are working on, the process should always start with asking the right research question. A perfect research question should be specific, clear, focused (meaning not too broad), and manageable.

Example: How does eating fruits and vegetables affect human health?

2. Conduct Your Basic Initial Research

As you already know, a hypothesis is an educated guess of the expected results and outcomes of an investigation. Thus, it is vital to collect some information before you can make this assumption.

At this stage, you should find an answer to your research question based on what has already been discovered. Search for facts, past studies, theories, etc. Based on the collected information, you should be able to make a logical and intelligent guess.

3. Formulate a Hypothesis

Based on the initial research, you should have a certain idea of what you may find throughout the course of your research. Use this knowledge to shape a clear and concise hypothesis.

Based on the type of project you are working on, and the type of hypothesis you are planning to use, you can restate your hypothesis in several different ways:

Non-directional: Eating fruits and vegetables will affect one’s human physical health. Directional: Eating fruits and vegetables will positively affect one’s human physical health. Null: Eating fruits and vegetables will have no effect on one’s human physical health.

4. Refine Your Hypothesis

Finally, the last stage of creating a good hypothesis is refining what you’ve got. During this step, you need to define whether your hypothesis:

• Has clear and relevant variables;
• Identifies the relationship between its variables;
• Is specific and testable;
• Suggests a predicted result of the investigation or experiment.

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Hypothesis Examples

Following a step-by-step guide and tips from our essay writers for hire , you should be able to create good hypotheses with ease. To give you a starting point, we have also compiled a list of different research questions with one hypothesis and one null hypothesis example for each:

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Sometimes, coping with a large academic load is just too much for a student to handle. Papers like research papers and dissertations can take too much time and effort to write, and, often, a hypothesis is a necessary starting point to get the task on track. Writing or editing a hypothesis is not as easy as it may seem. However, if you need help with forming it, the team at EssayPro is always ready to come to your rescue! If you’re feeling stuck, or don’t have enough time to cope with other tasks, don’t hesitate to send us you rewrite my essay for me or any other request.

is an expert in nursing and healthcare, with a strong background in history, law, and literature. Holding advanced degrees in nursing and public health, his analytical approach and comprehensive knowledge help students navigate complex topics. On EssayPro blog, Adam provides insightful articles on everything from historical analysis to the intricacies of healthcare policies. In his downtime, he enjoys historical documentaries and volunteering at local clinics.

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What Is a Hypothesis and How Do I Write One?

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

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Hypothesis Examples

A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method . A good hypothesis is testable, meaning it makes a prediction you can check with observation or experimentation. Here are different hypothesis examples.

Null Hypothesis Examples

The null hypothesis (H 0 ) is also known as the zero-difference or no-difference hypothesis. It predicts that changing one variable ( independent variable ) will have no effect on the variable being measured ( dependent variable ). Here are null hypothesis examples:

• Plant growth is unaffected by temperature.
• If you increase temperature, then solubility of salt will increase.
• Incidence of skin cancer is unrelated to ultraviolet light exposure.
• All brands of light bulb last equally long.
• Cats have no preference for the color of cat food.
• All daisies have the same number of petals.

Sometimes the null hypothesis shows there is a suspected correlation between two variables. For example, if you think plant growth is affected by temperature, you state the null hypothesis: “Plant growth is not affected by temperature.” Why do you do this, rather than say “If you change temperature, plant growth will be affected”? The answer is because it’s easier applying a statistical test that shows, with a high level of confidence, a null hypothesis is correct or incorrect.

Research Hypothesis Examples

A research hypothesis (H 1 ) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it’s easy identifying the independent and dependent variables and seeing how one affects the other. If-then statements explore cause and effect. In other cases, the hypothesis shows a correlation between two variables. Here are some research hypothesis examples:

• If you leave the lights on, then it takes longer for people to fall asleep.
• If you refrigerate apples, they last longer before going bad.
• If you keep the curtains closed, then you need less electricity to heat or cool the house (the electric bill is lower).
• If you leave a bucket of water uncovered, then it evaporates more quickly.
• Goldfish lose their color if they are not exposed to light.
• Workers who take vacations are more productive than those who never take time off.

Is It Okay to Disprove a Hypothesis?

Yes! You may even choose to write your hypothesis in such a way that it can be disproved because it’s easier to prove a statement is wrong than to prove it is right. In other cases, if your prediction is incorrect, that doesn’t mean the science is bad. Revising a hypothesis is common. It demonstrates you learned something you did not know before you conducted the experiment.

Test yourself with a Scientific Method Quiz .

• Mellenbergh, G.J. (2008). Chapter 8: Research designs: Testing of research hypotheses. In H.J. Adèr & G.J. Mellenbergh (eds.), Advising on Research Methods: A Consultant’s Companion . Huizen, The Netherlands: Johannes van Kessel Publishing.
• Popper, Karl R. (1959). The Logic of Scientific Discovery . Hutchinson & Co. ISBN 3-1614-8410-X.
• Schick, Theodore; Vaughn, Lewis (2002). How to think about weird things: critical thinking for a New Age . Boston: McGraw-Hill Higher Education. ISBN 0-7674-2048-9.
• Tobi, Hilde; Kampen, Jarl K. (2018). “Research design: the methodology for interdisciplinary research framework”. Quality & Quantity . 52 (3): 1209–1225. doi: 10.1007/s11135-017-0513-8

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• Null and Alternative Hypotheses | Definitions & Examples

Null & Alternative Hypotheses | Definitions, Templates & Examples

Published on May 6, 2022 by Shaun Turney . Revised on June 22, 2023.

The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test :

• Null hypothesis ( H 0 ): There’s no effect in the population .
• Alternative hypothesis ( H a or H 1 ) : There’s an effect in the population.

Table of contents

Answering your research question with hypotheses, what is a null hypothesis, what is an alternative hypothesis, similarities and differences between null and alternative hypotheses, how to write null and alternative hypotheses, other interesting articles, frequently asked questions.

The null and alternative hypotheses offer competing answers to your research question . When the research question asks “Does the independent variable affect the dependent variable?”:

• The null hypothesis ( H 0 ) answers “No, there’s no effect in the population.”
• The alternative hypothesis ( H a ) answers “Yes, there is an effect in the population.”

The null and alternative are always claims about the population. That’s because the goal of hypothesis testing is to make inferences about a population based on a sample . Often, we infer whether there’s an effect in the population by looking at differences between groups or relationships between variables in the sample. It’s critical for your research to write strong hypotheses .

You can use a statistical test to decide whether the evidence favors the null or alternative hypothesis. Each type of statistical test comes with a specific way of phrasing the null and alternative hypothesis. However, the hypotheses can also be phrased in a general way that applies to any test.

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The null hypothesis is the claim that there’s no effect in the population.

If the sample provides enough evidence against the claim that there’s no effect in the population ( p ≤ α), then we can reject the null hypothesis . Otherwise, we fail to reject the null hypothesis.

Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept . Be careful not to say you “prove” or “accept” the null hypothesis.

Null hypotheses often include phrases such as “no effect,” “no difference,” or “no relationship.” When written in mathematical terms, they always include an equality (usually =, but sometimes ≥ or ≤).

You can never know with complete certainty whether there is an effect in the population. Some percentage of the time, your inference about the population will be incorrect. When you incorrectly reject the null hypothesis, it’s called a type I error . When you incorrectly fail to reject it, it’s a type II error.

Examples of null hypotheses

The table below gives examples of research questions and null hypotheses. There’s always more than one way to answer a research question, but these null hypotheses can help you get started.

*Note that some researchers prefer to always write the null hypothesis in terms of “no effect” and “=”. It would be fine to say that daily meditation has no effect on the incidence of depression and p 1 = p 2 .

The alternative hypothesis ( H a ) is the other answer to your research question . It claims that there’s an effect in the population.

Often, your alternative hypothesis is the same as your research hypothesis. In other words, it’s the claim that you expect or hope will be true.

The alternative hypothesis is the complement to the null hypothesis. Null and alternative hypotheses are exhaustive, meaning that together they cover every possible outcome. They are also mutually exclusive, meaning that only one can be true at a time.

Alternative hypotheses often include phrases such as “an effect,” “a difference,” or “a relationship.” When alternative hypotheses are written in mathematical terms, they always include an inequality (usually ≠, but sometimes < or >). As with null hypotheses, there are many acceptable ways to phrase an alternative hypothesis.

Examples of alternative hypotheses

The table below gives examples of research questions and alternative hypotheses to help you get started with formulating your own.

Null and alternative hypotheses are similar in some ways:

• They’re both answers to the research question.
• They both make claims about the population.
• They’re both evaluated by statistical tests.

However, there are important differences between the two types of hypotheses, summarized in the following table.

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To help you write your hypotheses, you can use the template sentences below. If you know which statistical test you’re going to use, you can use the test-specific template sentences. Otherwise, you can use the general template sentences.

General template sentences

The only thing you need to know to use these general template sentences are your dependent and independent variables. To write your research question, null hypothesis, and alternative hypothesis, fill in the following sentences with your variables:

Does independent variable affect dependent variable ?

• Null hypothesis ( H 0 ): Independent variable does not affect dependent variable.
• Alternative hypothesis ( H a ): Independent variable affects dependent variable.

Test-specific template sentences

Once you know the statistical test you’ll be using, you can write your hypotheses in a more precise and mathematical way specific to the test you chose. The table below provides template sentences for common statistical tests.

Note: The template sentences above assume that you’re performing one-tailed tests . One-tailed tests are appropriate for most studies.

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.

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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.

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.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“ x affects y because …”).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses . In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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How to Write a Hypothesis

Often, one of the trickiest parts of designing and writing up any research paper is writing the hypothesis.

This article is a part of the guide:

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• 1 Write a Research Paper
• 2 Writing a Paper
• 3.1 Write an Outline
• 3.2 Outline Examples
• 4.1 Thesis Statement
• 4.2 Write a Hypothesis
• 5.2 Abstract
• 5.3 Introduction
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The entire experiment revolves around the research hypothesis (H 1 ) and the null hypothesis (H 0 ), so making a mistake here could ruin the whole design .

Needless to say, it can all be a little intimidating, and many students find this to be the most difficult stage of the scientific method .

In fact, it is not as difficult as it looks, and if you have followed the steps of the scientific process and found an area of research and potential research problem , then you may already have a few ideas.

It is just about making sure that you are asking the right questions and wording your hypothesis statements correctly.

Once you have nailed down a promising hypothesis, the rest of the process will flow a lot more easily.

The Three-Step Process

It can quite difficult to isolate a testable hypothesis after all of the research and study. The best way is to adopt a three-step hypothesis; this will help you to narrow things down, and is the most foolproof guide to how to write a hypothesis.

Step one is to think of a general hypothesis, including everything that you have observed and reviewed during the information gathering stage of any research design . This stage is often called developing the research problem .

An Example of How to Write a Hypothesis

A worker on a fish-farm notices that his trout seem to have more fish lice in the summer, when the water levels are low, and wants to find out why. His research leads him to believe that the amount of oxygen is the reason - fish that are oxygen stressed tend to be more susceptible to disease and parasites.

He proposes a general hypothesis.

“Water levels affect the amount of lice suffered by rainbow trout.”

This is a good general hypothesis, but it gives no guide to how to design the research or experiment . The hypothesis must be refined to give a little direction.

“Rainbow trout suffer more lice when water levels are low.”

Now there is some directionality, but the hypothesis is not really testable , so the final stage is to design an experiment around which research can be designed, i.e. a testable hypothesis.

“Rainbow trout suffer more lice in low water conditions because there is less oxygen in the water.”

This is a testable hypothesis - he has established variables , and by measuring the amount of oxygen in the water, eliminating other controlled variables , such as temperature, he can see if there is a correlation against the number of lice on the fish.

This is an example of how a gradual focusing of research helps to define how to write a hypothesis .

The Next Stage - What to Do with the Hypothesis

Once you have your hypothesis , the next stage is to design the experiment , allowing a statistical analysis of data, and allowing you to test your hypothesis .

The statistical analysis will allow you to reject either the null or the alternative hypothesis. If the alternative is rejected, then you need to go back and refine the initial hypothesis or design a completely new research program.

This is part of the scientific process, striving for greater accuracy and developing ever more refined hypotheses.

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Writing a Strong Hypothesis Statement

All good theses begins with a good thesis question. However, all great theses begins with a great hypothesis statement. One of the most important steps for writing a thesis is to create a strong hypothesis statement. 

What is a hypothesis statement?

A hypothesis statement must be testable. If it cannot be tested, then there is no research to be done.

Simply put, a hypothesis statement posits the relationship between two or more variables. It is a prediction of what you think will happen in a research study. A hypothesis statement must be testable. If it cannot be tested, then there is no research to be done. If your thesis question is whether wildfires have effects on the weather, “wildfires create tornadoes” would be your hypothesis. However, a hypothesis needs to have several key elements in order to meet the criteria for a good hypothesis.

In this article, we will learn about what distinguishes a weak hypothesis from a strong one. We will also learn how to phrase your thesis question and frame your variables so that you are able to write a strong hypothesis statement and great thesis.

What is a hypothesis?

A hypothesis statement posits, or considers, a relationship between two variables.

As we mentioned above, a hypothesis statement posits or considers a relationship between two variables. In our hypothesis statement example above, the two variables are wildfires and tornadoes, and our assumed relationship between the two is a causal one (wildfires cause tornadoes). It is clear from our example above what we will be investigating: the relationship between wildfires and tornadoes.

A strong hypothesis statement should be:

• A prediction of the relationship between two or more variables

A hypothesis is not just a blind guess. It should build upon existing theories and knowledge . Tornadoes are often observed near wildfires once the fires reach a certain size. In addition, tornadoes are not a normal weather event in many areas; they have been spotted together with wildfires. This existing knowledge has informed the formulation of our hypothesis.

Depending on the thesis question, your research paper might have multiple hypothesis statements. What is important is that your hypothesis statement or statements are testable through data analysis, observation, experiments, or other methodologies.

Formulating your hypothesis

One of the best ways to form a hypothesis is to think about “if...then” statements.

Now that we know what a hypothesis statement is, let’s walk through how to formulate a strong one. First, you will need a thesis question. Your thesis question should be narrow in scope, answerable, and focused. Once you have your thesis question, it is time to start thinking about your hypothesis statement. You will need to clearly identify the variables involved before you can begin thinking about their relationship.

One of the best ways to form a hypothesis is to think about “if...then” statements . This can also help you easily identify the variables you are working with and refine your hypothesis statement. Let’s take a few examples.

If teenagers are given comprehensive sex education, there will be fewer teen pregnancies .

In this example, the independent variable is whether or not teenagers receive comprehensive sex education (the cause), and the dependent variable is the number of teen pregnancies (the effect).

If a cat is fed a vegan diet, it will die .

Here, our independent variable is the diet of the cat (the cause), and the dependent variable is the cat’s health (the thing impacted by the cause).

If children drink 8oz of milk per day, they will grow taller than children who do not drink any milk .

What are the variables in this hypothesis? If you identified drinking milk as the independent variable and growth as the dependent variable, you are correct. This is because we are guessing that drinking milk causes increased growth in the height of children.

Refining your hypothesis

Do not be afraid to refine your hypothesis throughout the process of formulation.

Do not be afraid to refine your hypothesis throughout the process of formulation. A strong hypothesis statement is clear, testable, and involves a prediction. While “testable” means verifiable or falsifiable, it also means that you are able to perform the necessary experiments without violating any ethical standards. Perhaps once you think about the ethics of possibly harming some cats by testing a vegan diet on them you might abandon the idea of that experiment altogether. However, if you think it is really important to research the relationship between a cat’s diet and a cat’s health, perhaps you could refine your hypothesis to something like this:

If 50% of a cat’s meals are vegan, the cat will not be able to meet its nutritional needs .

Another feature of a strong hypothesis statement is that it can easily be tested with the resources that you have readily available. While it might not be feasible to measure the growth of a cohort of children throughout their whole lives, you may be able to do so for a year. Then, you can adjust your hypothesis to something like this:

I f children aged 8 drink 8oz of milk per day for one year, they will grow taller during that year than children who do not drink any milk .

As you work to narrow down and refine your hypothesis to reflect a realistic potential research scope, don’t be afraid to talk to your supervisor about any concerns or questions you might have about what is truly possible to research. 

What makes a hypothesis weak?

We noted above that a strong hypothesis statement is clear, is a prediction of a relationship between two or more variables, and is testable. We also clarified that statements, which are too general or specific are not strong hypotheses. We have looked at some examples of hypotheses that meet the criteria for a strong hypothesis, but before we go any further, let’s look at weak or bad hypothesis statement examples so that you can really see the difference.

Bad hypothesis 1: Diabetes is caused by witchcraft .

While this is fun to think about, it cannot be tested or proven one way or the other with clear evidence, data analysis, or experiments. This bad hypothesis fails to meet the testability requirement.

Bad hypothesis 2: If I change the amount of food I eat, my energy levels will change .

This is quite vague. Am I increasing or decreasing my food intake? What do I expect exactly will happen to my energy levels and why? How am I defining energy level? This bad hypothesis statement fails the clarity requirement.

Bad hypothesis 3: Japanese food is disgusting because Japanese people don’t like tourists .

This hypothesis is unclear about the posited relationship between variables. Are we positing the relationship between the deliciousness of Japanese food and the desire for tourists to visit? or the relationship between the deliciousness of Japanese food and the amount that Japanese people like tourists? There is also the problematic subjectivity of the assessment that Japanese food is “disgusting.” The problems are numerous.

The null hypothesis and the alternative hypothesis

The null hypothesis, quite simply, posits that there is no relationship between the variables.

What is the null hypothesis?

The hypothesis posits a relationship between two or more variables. The null hypothesis, quite simply, posits that there is no relationship between the variables. It is often indicated as H 0 , which is read as “h-oh” or “h-null.” The alternative hypothesis is the opposite of the null hypothesis as it posits that there is some relationship between the variables. The alternative hypothesis is written as H a or H 1 .

Let’s take our previous hypothesis statement examples discussed at the start and look at their corresponding null hypothesis.

H a : If teenagers are given comprehensive sex education, there will be fewer teen pregnancies .

H 0 : If teenagers are given comprehensive sex education, there will be no change in the number of teen pregnancies .

The null hypothesis assumes that comprehensive sex education will not affect how many teenagers get pregnant. It should be carefully noted that the null hypothesis is not always the opposite of the alternative hypothesis. For example:

If teenagers are given comprehensive sex education, there will be more teen pregnancies .

These are opposing statements that assume an opposite relationship between the variables: comprehensive sex education increases or decreases the number of teen pregnancies. In fact, these are both alternative hypotheses. This is because they both still assume that there is a relationship between the variables . In other words, both hypothesis statements assume that there is some kind of relationship between sex education and teen pregnancy rates. The alternative hypothesis is also the researcher’s actual predicted outcome, which is why calling it “alternative” can be confusing! However, you can think of it this way: our default assumption is the null hypothesis, and so any possible relationship is an alternative to the default.

Step-by-step sample hypothesis statements

Now that we’ve covered what makes a hypothesis statement strong, how to go about formulating a hypothesis statement, refining your hypothesis statement, and the null hypothesis, let’s put it all together with some examples. The table below shows a breakdown of how we can take a thesis question, identify the variables, create a null hypothesis, and finally create a strong alternative hypothesis.

Once you have formulated a solid thesis question and written a strong hypothesis statement, you are ready to begin your thesis in earnest. Check out our site for more tips on writing a great thesis and information on thesis proofreading and editing services.

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Start with a clear thesis question

Think about “if-then” statements to identify your variables and the relationship between them

Create a null hypothesis

Formulate an alternative hypothesis using the variables you have identified

Make sure your hypothesis clearly posits a relationship between variables

Make sure your hypothesis is testable considering your available time and resources

What makes a hypothesis strong? +

A hypothesis is strong when it is testable, clear, and identifies a potential relationship between two or more variables.

What makes a hypothesis weak? +

A hypothesis is weak when it is too specific or too general, or does not identify a clear relationship between two or more variables.

What is the null hypothesis? +

The null hypothesis posits that the variables you have identified have no relationship.

What Are Examples of a Hypothesis?

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A hypothesis is an explanation for a set of observations. Hypothesis examples can help you understand how this scientific method works.

Although you could state a scientific hypothesis in various ways, most hypotheses are either "If, then" statements or forms of the null hypothesis. The null hypothesis is sometimes called the "no difference" hypothesis. The null hypothesis is good for experimentation because it's simple to disprove. If you disprove a null hypothesis, that is evidence for a relationship between the variables you are examining.

Hypotheses Examples: Null

• All daisies have the same number of petals.
• Hyperactivity is unrelated to eating sugar.
• The number of pets in a household is unrelated to the number of people living in it.
• A person's preference for a shirt is unrelated to its color.

Hypotheses Examples: If, Then

• If you get at least 6 hours of sleep, you will do better on tests than if you get less sleep.
• If you drop a ball, it will fall toward the ground.
• If you drink coffee before going to bed, then it will take longer to fall asleep.
• If you cover a wound with a bandage, then it will heal with less scarring.

Improving a Hypothesis to Make It Testable

You may wish to revise your first hypothesis to make it easier to design an experiment to test. For example, let's say you have a bad breakout the morning after eating a lot of greasy food. You may wonder if there is a correlation between eating greasy food and getting pimples. You propose the hypothesis example:

Eating greasy food causes pimples.

Next, you need to design an experiment to test this hypothesis. Let's say you decide to eat greasy food every day for a week and record the effect on your face. Then, as a control, you'll avoid greasy food for the next week and see what happens. Now, this is not a good experiment because it does not take into account other factors such as hormone levels, stress, sun exposure, exercise, or any number of other variables that might conceivably affect your skin.

The problem is that you cannot assign cause to your effect . If you eat french fries for a week and suffer a breakout, can you definitely say it was the grease in the food that caused it? Maybe it was the salt. Maybe it was the potato. Maybe it was unrelated to diet. You can't prove your hypothesis. It's much easier to disprove a hypothesis.

So, let's restate the hypothesis to make it easier to evaluate the data:

Getting pimples is unaffected by eating greasy food.

So, if you eat fatty food every day for a week and suffer breakouts and then don't break out the week that you avoid greasy food, you can be pretty sure something is up. Can you disprove the hypothesis? Probably not, since it is so hard to assign cause and effect. However, you can make a strong case that there is some relationship between diet and acne.

If your skin stays clear for the entire test, you may decide to accept your hypothesis . Again, you didn't prove or disprove anything, which is fine

• Null Hypothesis Examples
• The Role of a Controlled Variable in an Experiment
• Random Error vs. Systematic Error
• What Is a Testable Hypothesis?
• What Are the Elements of a Good Hypothesis?
• Scientific Hypothesis Examples
• What Is a Hypothesis? (Science)
• Scientific Method Vocabulary Terms
• Scientific Method Flow Chart
• Understanding Simple vs Controlled Experiments
• Six Steps of the Scientific Method
• What Is an Experimental Constant?
• What Is the Difference Between a Control Variable and Control Group?
• Scientific Variable
• What Is a Controlled Experiment?
• DRY MIX Experiment Variables Acronym