5 Characteristics of a Good Hypothesis: A Guide for Researchers
- by Brian Thomas
- October 4, 2024
Are you a curious soul, always seeking answers to the whys and hows of the world? As a researcher, formulating a hypothesis is a crucial first step towards unraveling the mysteries of your study. A well-crafted hypothesis not only guides your research but also lays the foundation for drawing valid conclusions. But what exactly makes a hypothesis a good one? In this blog post, we will explore the five key characteristics of a good hypothesis that every researcher should know.
Here, we will delve into the world of hypotheses, covering everything from their types in research to understanding if they can be proven true. Whether you’re a seasoned researcher or just starting out, this blog post will provide valuable insights on how to craft a sound hypothesis for your study. So let’s dive in and uncover the secrets to formulating a hypothesis that stands strong amidst the scientific rigor!
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5 Characteristics of a Good Hypothesis
Clear and specific.
A good hypothesis is like a GPS that guides you to the right destination. It needs to be clear and specific so that you know exactly what you’re testing. Avoid vague statements or general ideas. Instead, focus on crafting a hypothesis that clearly states the relationship between variables and the expected outcome. Clarity is key, my friend!
Testable and Falsifiable
A hypothesis might sound great in theory, but if you can’t test it or prove it wrong, then it’s like chasing unicorns. A good hypothesis should be testable and falsifiable – meaning there should be a way to gather evidence to support or refute it. Don’t be afraid to challenge your hypothesis and put it to the test. Only when it can be proven false can it truly be considered a good hypothesis.
Based on Existing Knowledge
Imagine trying to build a Lego tower without any Lego bricks. That’s what it’s like to come up with a hypothesis that has no basis in existing knowledge. A good hypothesis is grounded in previous research, theories, or observations. It shows that you’ve done your homework and understand the current state of knowledge in your field. So, put on your research hat and gather those building blocks for a solid hypothesis!
Specific Predictions
No, we’re not talking about crystal ball predictions or psychic abilities here. A good hypothesis includes specific predictions about what you expect to happen. It’s like making an educated guess based on your understanding of the variables involved. These predictions help guide your research and give you something concrete to look for. So, put on those prediction goggles, my friend, and let’s get specific!
Relevant to the Research Question
A hypothesis is a road sign that points you in the right direction. But if it’s not relevant to your research question, then you might end up in a never-ending detour. A good hypothesis aligns with your research question and addresses the specific problem or phenomenon you’re investigating. Keep your focus on the main topic and avoid getting sidetracked by shiny distractions. Stay relevant, my friend, and you’ll find the answers you seek!
And there you have it: the five characteristics of a good hypothesis. Remember, a good hypothesis is clear, testable, based on existing knowledge, makes specific predictions, and is relevant to your research question. So go forth, my friend, and hypothesize your way to scientific discovery!
FAQs: Characteristics of a Good Hypothesis
In the realm of scientific research, a hypothesis plays a crucial role in formulating and testing ideas. A good hypothesis serves as the foundation for an experiment or study, guiding the researcher towards meaningful results. In this FAQ-style subsection, we’ll explore the characteristics of a good hypothesis, their types, formulation, and more. So let’s dive in and unravel the mysteries of hypothesis-making!
What Are Two Important Characteristics of a Good Hypothesis
A good hypothesis possesses two important characteristics:
Testability : A hypothesis must be testable to determine its validity. It should be formulated in a way that allows researchers to design and conduct experiments or gather data for analysis. For example, if we hypothesize that “drinking herbal tea reduces stress,” we can easily test it by conducting a study with a control group and a group drinking herbal tea.
Falsifiability : Falsifiability refers to the potential for a hypothesis to be proven wrong. A good hypothesis should make specific predictions that can be refuted or supported by evidence. This characteristic ensures that hypotheses are based on empirical observations rather than personal opinions. For instance, the hypothesis “all swans are white” can be falsified by discovering a single black swan.
What Are the Types of Hypothesis in Research
In research, there are three main types of hypotheses:
Null Hypothesis (H0) : The null hypothesis is a statement of no effect or relationship. It assumes that there is no significant difference between variables or no effect of a treatment. Researchers aim to reject the null hypothesis in favor of an alternative hypothesis.
Alternative Hypothesis (HA or H1) : The alternative hypothesis is the opposite of the null hypothesis. It asserts that there is a significant difference between variables or an effect of a treatment. Researchers seek evidence to support the alternative hypothesis.
Directional Hypothesis : A directional hypothesis predicts the specific direction of the relationship or difference between variables. For example, “increasing exercise duration will lead to greater weight loss.”
Can a Hypothesis Be Proven True
In scientific research, hypotheses are not proven true; they are supported or rejected based on empirical evidence . Even if a hypothesis is supported by multiple studies, new evidence could arise that contradicts it. Scientific knowledge is always subject to revision and refinement. Therefore, the goal is to gather enough evidence to either support or reject a hypothesis, rather than proving it absolutely true.
What Are the Six Parts of a Hypothesis
A hypothesis typically consists of six essential parts:
Research Question : A clear and concise question that the hypothesis seeks to answer.
Variables : Identification of the independent (manipulated) and dependent (measured) variables involved in the hypothesis.
Population : The specific group or individuals the hypothesis is concerned with.
Relationship or Comparison : The expected relationship or difference between variables, often indicated by directional terms like “more,” “less,” “higher,” or “lower.”
Predictability : A statement of the predicted outcome or result based on the relationship between variables.
Testability : The ability to design an experiment or gather data to support or reject the hypothesis.
How Do You Start a Hypothesis Sentence
When starting a hypothesis sentence, it is essential to use clear and concise language to express your ideas. A common approach is to use the phrase “If…then…” to establish the conditional relationship between variables. For example:
- If [independent variable], then [dependent variable] because [explanation of expected relationship].
This structure allows for a straightforward and logical formulation of the hypothesis.
What Are Examples of Hypotheses
Here are a few examples of well-formulated hypotheses:
If exposure to sunlight increases, then plants will grow taller because sunlight is necessary for photosynthesis.
If students receive praise for good grades, then their motivation to excel will increase because they seek recognition and approval.
If the dose of a painkiller is increased, then the relief from pain will last longer because a higher dosage has a prolonged effect.
What Are the Five Key Elements to a Good Hypothesis
A good hypothesis should include the following five key elements:
Clarity : The hypothesis should be clear and specific, leaving no room for interpretation.
Testability : It should be possible to test the hypothesis through experimentation or data collection.
Relevance : The hypothesis should be directly tied to the research question or problem being investigated.
Specificity : It must clearly state the relationship or difference between variables being studied.
Falsifiability : The hypothesis should make predictions that can be refuted or supported by empirical evidence.
What Makes a Good Hypothesis in a Research Paper
In a research paper, a good hypothesis should have the following characteristics:
Relevance : It must directly relate to the research topic and address the objectives of the study.
Clarity : The hypothesis should be concise and precisely worded to avoid confusion.
Unambiguous : It must leave no room for multiple interpretations or ambiguity.
Logic : The hypothesis should be based on rational and logical reasoning, considering existing theories and observations.
Empirical Support : Ideally, the hypothesis should be supported by prior empirical evidence or strong theoretical justifications.
Is a Hypothesis Always a Question
No, a hypothesis is not always in the form of a question. While some hypotheses can take the form of a question, others may be statements asserting a relationship or difference between variables. The form of a hypothesis depends on the research question being addressed and the researcher’s preferred style of expression.
What Are the Three Things Needed for a Good Hypothesis
For a hypothesis to be considered good, it must fulfill the following three criteria:
Testability : The hypothesis should be formulated in a way that allows for empirical testing through experimentation or data collection.
Falsifiability : It must make specific predictions that can be potentially refuted or supported by evidence.
Relevance : The hypothesis should directly address the research question or problem being investigated.
What Are the Four Components to a Good Hypothesis
A good hypothesis typically consists of four components:
Independent Variable : The variable being manipulated or controlled by the researcher.
Dependent Variable : The variable being measured or observed to determine the effect of the independent variable.
Directionality : The predicted relationship or difference between the independent and dependent variables.
Population : The specific group or individuals to which the hypothesis applies.
How Do You Formulate a Hypothesis
To formulate a hypothesis, follow these steps:
Identify the Research Topic : Clearly define the area or phenomenon you want to study.
Conduct Background Research : Review existing literature and research to gain knowledge about the topic.
Formulate a Research Question : Ask a clear and focused question that you want to answer through your hypothesis.
State the Null and Alternative Hypotheses : Develop a null hypothesis to assume no effect or relationship, and an alternative hypothesis to propose a significant effect or relationship.
Decide on Variables and Relationships : Determine the independent and dependent variables and the predicted relationship between them.
Refine and Test : Refine your hypothesis, ensuring it is clear, testable, and falsifiable. Then, design experiments or gather data to support or reject it.
What Is a Characteristic of a Hypothesis MCQ
Multiple-choice questions (MCQ) regarding the characteristics of a hypothesis often assess knowledge on the testability and falsifiability of hypotheses. They may ask about the criteria that distinguish a good hypothesis from a poor one or the importance of making specific predictions. Remember to choose answers that emphasize the empirical and testable nature of hypotheses.
What Five Criteria Must Be Satisfied for a Hypothesis to Be Scientific
For a hypothesis to be considered scientific, it must satisfy the following five criteria:
Testability : The hypothesis must be formulated in a way that allows it to be tested through experimentation or data collection.
Falsifiability : It should make specific predictions that can be potentially refuted or supported by empirical evidence.
Empirical Basis : The hypothesis should be based on empirical observations or existing theories and knowledge.
Relevance : It must directly address the research question or problem being investigated.
Objective : A scientific hypothesis should be free from personal biases or subjective opinions, focusing on objective observations and analysis.
What Are the Steps of Theory Development in Scientific Methods
In scientific methods, theory development typically involves the following steps:
Observation : Identifying a phenomenon or pattern worthy of investigation through observation or empirical data.
Formulation of a Hypothesis : Constructing a hypothesis that explains the observed phenomena or predicts a relationship between variables.
Data Collection : Gathering relevant data through experiments, surveys, observations, or other research methods.
Analysis : Analyzing the collected data to evaluate the hypothesis’s predictions and determine their validity.
Revision and Refinement : Based on the analysis, refining the hypothesis, modifying the theory, or formulating new hypotheses for further investigation.
Which of the Following Makes a Good Hypothesis
A good hypothesis is characterized by:
Testability : The ability to form experiments or gather data to support or refute the hypothesis.
Falsifiability : The potential for the hypothesis’s predictions to be proven wrong based on empirical evidence.
Clarity : A clear and concise statement or question that leaves no room for ambiguity.
Relevancy : Directly addressing the research question or problem at hand.
Remember, it is important to select the option that encompasses all these characteristics.
What Are the Characteristics of a Good Hypothesis
A good hypothesis possesses several characteristics, such as:
Testability : It should allow for empirical testing through experiments or data collection.
Falsifiability : The hypothesis should make specific predictions that can be potentially refuted or supported by evidence.
Clarity : It must be clearly and precisely formulated, leaving no room for ambiguity or multiple interpretations.
Relevance : The hypothesis should directly relate to the research question or problem being investigated.
What Is the Five-Step p-value Approach to Hypothesis Testing
The five-step p-value approach is a commonly used framework for hypothesis testing:
Step 1: Formulating the Hypotheses : The null hypothesis (H0) assumes no effect or relationship, while the alternative hypothesis (HA) proposes a significant effect or relationship.
Step 2: Setting the Significance Level : Decide on the level of significance (α), which represents the probability of rejecting the null hypothesis when it is true. The commonly used level is 0.05 (5%).
Step 3: Collecting Data and Performing the Test : Acquire and analyze the data, calculating the test statistic and the corresponding p-value.
Step 4: Comparing the p-value with the Significance Level : If the p-value is less than the significance level (α), reject the null hypothesis. Otherwise, fail to reject the null hypothesis.
Step 5: Drawing Conclusions : Based on the comparison in Step 4, interpret the results and draw conclusions about the hypothesis.
What Are the Stages of Hypothesis
The stages of hypothesis generally include:
Observation : Identifying a pattern, phenomenon, or research question that warrants investigation.
Formulation : Developing a hypothesis that explains or predicts the relationship or difference between variables.
Testing : Collecting data, designing experiments, or conducting studies to gather evidence supporting or refuting the hypothesis.
Analysis : Assessing the collected data to determine whether the results support or reject the hypothesis.
Conclusion : Drawing conclusions based on the analysis and making further iterations, refinements, or new hypotheses for future research.
What Is a Characteristic of a Good Hypothesis
A characteristic of a good hypothesis is its ability to make specific predictions about the relationship or difference between variables. Good hypotheses avoid vague statements and clearly articulate the expected outcomes. By doing so, researchers can design experiments or gather data that directly test the predictions, leading to meaningful results.
How Do You Write a Good Hypothesis Example
To write a good hypothesis example, follow these guidelines:
If possible, use the “If…then…” format to express a conditional relationship between variables.
Be clear and concise in stating the variables involved, the predicted relationship, and the expected outcome.
Ensure the hypothesis is testable, meaning it can be evaluated through experiments or data collection.
For instance, consider the following example:
If students study for longer periods of time, then their test scores will improve because increased study time allows for better retention of information and increased proficiency.
What Is the Difference Between Hypothesis and Hypotheses
The main difference between a hypothesis and hypotheses lies in their grammatical number. A hypothesis refers to a single statement or proposition that is formulated to explain or predict the relationship between variables. On the other hand, hypotheses is the plural form of the term hypothesis, commonly used when multiple statements or propositions are proposed and tested simultaneously.
What Is a Good Hypothesis Statement
A good hypothesis statement exhibits the following qualities:
Clarity : It is written in clear and concise language, leaving no room for confusion or ambiguity.
Testability : The hypothesis should be formulated in a way that enables testing through experiments or data collection.
Specificity : It must clearly state the predicted relationship or difference between variables.
By adhering to these criteria, a good hypothesis statement guides research efforts effectively.
What Is Not a Characteristic of a Good Hypothesis
A characteristic that does not align with a good hypothesis is subjectivity . A hypothesis should be objective, based on empirical observations or existing theories, and free from personal bias. While personal interpretations and opinions can inspire the formulation of a hypothesis, it must ultimately rely on objective observations and be open to empirical testing.
By now, you’ve gained insights into the characteristics of a good hypothesis, including testability, falsifiability, clarity,
- characteristics
- falsifiable
- good hypothesis
- hypothesis testing
- null hypothesis
- observations
- scientific rigor
Brian Thomas
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How to Write a Hypothesis: Step-By-Step Guide
A hypothesis is a testable statement that guides scientific research. Want to know how to write a hypothesis for your research paper? This guide will show you the key steps involved, including defining your variables and phrasing your hypothesis correctly.
Key Takeaways
- A hypothesis is a testable statement proposed for investigation, grounded in existing knowledge, essential for guiding scientific research.
- Understanding different types of hypotheses, including simple, complex, null, and alternative, is crucial for selecting appropriate research approaches.
- Crafting a strong hypothesis involves a systematic process including defining variables, phrasing it as an if-then statement, and ensuring it is clear, specific, and testable.
Understanding a Hypothesis
An empirical hypothesis is not just a simple guess. It represents a preliminary concept that stands to be scrutinized through Research and experimentation. A well-constructed hypothesis is a fundamental component of the scientific method, guiding experiments and leading to conclusions. Within the realm of science, such hypotheses are crafted after an extensive examination of current knowledge, ensuring their foundation on already established evidence prior to beginning any new inquiry.
Essentially, a hypothesis in the scientific community must present itself as something capable of being tested, this characteristic distinguishes it from mere speculation by allowing its potential verification or falsification through methodical scrutiny. Hypotheses serve as crucial instruments within scientific studies, directing these investigations toward particular queries and forming the backbone upon which all experiments rest in their pursuit for advancements in comprehension.
When formulating a hypothesis for testing within research activities, one should employ language that remains neutral and detached from subjective bias thereby bolstering the legitimacy of outcomes produced during the study. This precision fosters greater confidence in results obtained under rigorous evaluation standards among peers.
Characteristics of a Good Hypothesis
A good hypothesis is the cornerstone of any successful scientific research. It should be clear, concise, and testable, providing a solid foundation for your investigation. Here are some key characteristics that define a good hypothesis:
- Clarity : A good hypothesis should be easy to understand and clearly state the expected outcome of the research. For example , “Increased exposure to sunlight will result in taller plant growth” is a clear and straightforward hypothesis.
- Conciseness : Avoid unnecessary complexity or jargon. A concise hypothesis is brief and to the point, making it easier to test and analyze. For instance, “Exercise improves mental health” is concise and direct.
- Testability : A good hypothesis must be testable and falsifiable, meaning it can be proven or disproven through scientific research methods. For example, “Consuming vitamin C reduces the duration of the common cold” is a testable hypothesis.
- Relevance : Ensure your hypothesis is relevant to the research question or problem and aligned with your research objectives. For example, if your research question is about the impact of diet on health, a relevant hypothesis could be “A high-fiber diet reduces the risk of heart disease.”
- Specificity : A good hypothesis should be specific and focused on a particular aspect of the research question. For example, “Daily meditation reduces stress levels in college students” is specific and targeted.
- Measurability : Your hypothesis should be measurable, meaning it can be quantified or observed. For example, “Regular physical activity lowers blood pressure” is a measurable hypothesis.
By ensuring your hypothesis possesses these characteristics, you set a strong foundation for your scientific research, guiding your investigation towards meaningful and reliable results.
Types of Hypotheses
Scientific research incorporates a range of research hypotheses, which are crucial for proposing relationships between different variables and steering the direction of the investigation. These seven unique forms of hypotheses cater to diverse needs within the realm of scientific inquiry.
Comprehending these various types is essential in selecting an appropriate method for conducting research. To delve into details, we have simple, complex, null and alternative hypotheses. Each brings its distinct features and practical implications to the table. It underscores why recognizing how they diverge and what purposes they serve is fundamental in any scientific study.
Simple Hypothesis
A basic hypothesis suggests a fundamental relationship between two elements: the independent and dependent variable. Take, for example, a hypothesis that says, “The taller growth of plants (dependent variable) is due to increased exposure to sunlight (independent variable).” Such hypotheses are clear-cut and easily testable as they concentrate on one direct cause-and-effect link.
These types of straightforward hypotheses are very beneficial in scientific experiments because they permit the isolation of variables for precise outcome measurement. Their simplicity lends itself well to being an essential component in conducting scientific research, thanks to their unambiguous nature and targeted focus on specific relationships.
Complex Hypothesis
Alternatively, a complex hypothesis proposes an interconnection amongst several variables. It builds on the concept of numerous variable interactions within research parameters. Take for instance a causal hypothesis which asserts that sustained alcohol consumption (the independent variable) leads to liver impairment (the dependent variable), with additional influences like use duration and general health results impacting this relationship.
Involving various factors, complex hypotheses reveal the nuanced interaction of elements that affect results. Although they provide extensive insight into studied phenomena, such hypotheses necessitate advanced research frameworks and analysis techniques to be understood properly.
Null Hypothesis
In the realm of hypothesis testing, the null hypothesis (H0) serves as a fundamental presumption suggesting that there exists no association between the variables under investigation. It posits that variations within the dependent variable are attributed to random chance and not an influential relationship. Take for instance a null hypothesis which could propose “There is no impact of sleep duration on productivity levels.”
The significance of the null hypothesis lies in its role as a reference point which researchers strive to refute during their investigations. Upon uncovering statistical evidence indicative of a substantial linkage, it becomes necessary to discard the null hypothesis. The act of rejecting this foundational assumption is critical for affirming research findings and assessing their importance with respect to outcomes observed.
Alternative Hypothesis
The alternative hypothesis, often represented by H1 or Ha, contradicts the null hypothesis and proposes a meaningful link between variables under examination. For example, where the null hypothesis asserts that a particular medication is ineffective, the alternative might posit that “Compared to placebo treatment, the new drug yields beneficial effects.”
By claiming outcomes are non-random and carry weight, the alternative hypothesis bolsters theoretical assertions. Its testable prediction propels scientific investigation forward as it aims either to corroborate or debunk what’s posited by the null hypothesis.
Consider an assertive statement like “Productivity is influenced by sleep duration” which serves as a crisp articulation of an alternative hypothesis.
Steps to Write a Hypothesis
Crafting a hypothesis is a methodical process that begins with curiosity and culminates in a testable prediction. Writing a hypothesis involves following structured steps to ensure clarity, focus, and researchability. Steps include asking a research question, conducting preliminary research, defining variables, and phrasing the hypothesis as an if-then statement.
Each step is critical in formulating a strong hypothesis to guide research and lead to meaningful discoveries.
Ask a Research Question
A well-defined research question forms the cornerstone of a strong hypothesis, guiding your investigation towards a significant and targeted exploration. By rooting this question in observations and existing studies, it becomes pertinent and ripe for research. For example, noting that certain snacks are more popular could prompt the inquiry: “Does providing healthy snack options in an office setting enhance employee productivity?”.
Such a thoughtfully constructed question lays the groundwork for your research hypothesis, steering your scholarly work to be concentrated and purposeful.
Conduct Preliminary Research
Begin your research endeavor by conducting preliminary investigations into established theories, past studies, and available data. This initial stage is crucial as it equips you with a comprehensive background to craft an informed hypothesis while pinpointing any existing voids in current knowledge. Understanding the concept of a statistical hypothesis can also be beneficial, as it involves drawing conclusions about a population based on a sample and applying statistical evidence.
By reviewing literature and examining previously published research papers, one can discern the various variables of interest and their interconnections. Should the findings from these early inquiries refute your original hypothesis, adjust it accordingly so that it resonates with already recognized evidence.
Define Your Variables
A well-formed hypothesis should unambiguously identify the independent and dependent variables involved. In an investigation exploring how plant growth is affected by sunlight, for instance, plant height represents the dependent variable, while the quantity of sunlight exposure constitutes the independent variable.
It is essential to explicitly state all the variables included in a study so that the hypothesis can be tested with accuracy and specificity. Defining these variables distinctly facilitates a targeted and quantifiable examination.
Phrase as an If-Then Statement
A good hypothesis is typically structured in the form of if-then statements, allowing for a clear demonstration of the anticipated link between different variables. Take, for example, stating that administering drug X could result in reduced fatigue among patients. This outcome would be especially advantageous to individuals receiving cancer therapy. The structure aids in explicitly defining the cause-and-effect dynamic.
In order to craft a strong hypothesis, it should be capable of being tested and grounded on existing knowledge or theoretical frameworks. It should also be framed as a statement that can potentially be refuted by experimental data, which qualifies it as a solidly formulated hypothesis.
Collect Data to Support Your Hypothesis
Once you have formulated a hypothesis, the next crucial step is to collect data to support or refute it. This involves designing and conducting experiments or studies that test the hypothesis, and collecting and analyzing data to determine whether the hypothesis holds true.
Here are the key steps in collecting data to support your hypothesis:
- Designing an Experiment or Study : Start by identifying your research question or problem. Design a study or experiment that specifically tests your hypothesis. For example, if your hypothesis is “Daily exercise improves cognitive function,” design an experiment that measures cognitive function in individuals who exercise daily versus those who do not.
- Collecting Data : Gather data through various methods such as experiments, surveys, observations, or other techniques. Ensure your data collection methods are reliable and valid. For instance, use standardized tests to measure cognitive function in your exercise study.
- Analyzing Data : Use statistical methods or other techniques to analyze the data. This step involves determining whether the data supports or refutes your hypothesis. For example, use statistical tests to compare cognitive function scores between the exercise and non-exercise groups .
- Interpreting Results : Interpret the results of your data analysis to determine whether your hypothesis is supported. For instance, if the exercise group shows significantly higher cognitive function scores, your hypothesis is supported. If not, you may need to refine your hypothesis or explore other variables.
By following these steps, you can systematically collect and analyze data to support or refute your hypothesis, ensuring your research is grounded in empirical evidence.
Refining Your Hypothesis
To ensure your hypothesis is precise, comprehensible, verifiable, straightforward, and pertinent, you must refine it meticulously. Creating a compelling hypothesis involves careful consideration of its transparency, purposeful direction and the potential results. This requires unmistakably delineating the subject matter and central point of your experiment.
Your hypothesis should undergo stringent examination to remove any uncertainties and define parameters that guarantee both ethical integrity and scientific credibility. An effective hypothesis not only questions prevailing assumptions, but also maintains an ethically responsible framework.
Testing Your Hypothesis
Having a robust research methodology is essential for efficiently evaluating your hypothesis. It is important to ensure that the integrity and validity of the research are upheld through adherence to ethical standards. The data gathered ought to be both representative and tailored specifically towards validating or invalidating the hypothesis.
In order to ascertain whether there’s any significant difference, statistical analyses measure variations both within and across groups. Frequently, the decision on whether to discard the null hypothesis hinges on establishing a p-value cut-off point, which conventionally stands at 0.05.
Tips for Writing a Research Hypothesis
Writing a research hypothesis can be a challenging task, but with the right approach, you can craft a strong and testable hypothesis. Here are some tips to help you write a research hypothesis:
- Start with a Research Question : A good hypothesis starts with a clear and focused research question. For example, “Does regular exercise improve mental health?” can lead to a hypothesis like “Regular exercise reduces symptoms of depression.”
- Conduct Preliminary Research : Conducting preliminary research helps you identify a knowledge gap in your field and develop a hypothesis that is relevant and testable. Review existing literature and studies to inform your hypothesis.
- Use Clear and Concise Language : A good hypothesis should be easy to understand and use clear and concise language. Avoid jargon and complex terms. For example, “Increased screen time negatively impacts sleep quality” is clear and straightforward.
- Avoid Ambiguity and Vagueness : Ensure your hypothesis is free from ambiguity and vagueness. Clearly state the expected outcome of the research. For example, “Consuming caffeine before bedtime reduces sleep duration” is specific and unambiguous.
- Make Sure It Is Testable : A good hypothesis should be testable and falsifiable, meaning it can be proven or disproven through scientific research methods. For example, “A high-protein diet increases muscle mass” is a testable hypothesis.
- Use Existing Knowledge and Research : Base your hypothesis on existing knowledge and research. Align it with your research objectives and ensure it is grounded in established theories or findings.
Common mistakes to avoid when writing a research hypothesis include:
- Making It Too Broad or Too Narrow : A good hypothesis should be specific and focused on a particular aspect of the research question. Avoid overly broad or narrow hypotheses.
- Making It Too Vague or Ambiguous : Ensure your hypothesis is clear and concise, avoiding ambiguity and vagueness.
- Failing to Make It Testable : A good hypothesis should be testable and falsifiable. Ensure it can be proven or disproven through scientific research methods.
- Failing to Use Existing Knowledge and Research : Base your hypothesis on existing knowledge and research. Align it with your research objectives and ensure it is grounded in established theories or findings.
By following these tips and avoiding common mistakes, you can write a strong and testable research hypothesis that will guide your scientific investigation towards meaningful and reliable results.
Examples of Good and Bad Hypotheses
A well-constructed hypothesis is distinct, precise, and capable of being empirically verified. To be considered a good hypothesis, it must offer measurable and examinable criteria through experimental means. Take the claim “Working from home boosts job satisfaction” as an example. This posits a testable outcome related to work environments.
On the other hand, a subpar hypothesis such as “Garlic repels vampires” falls short because it hinges on fantastical elements that cannot be substantiated or refuted in reality. The ability to distinguish between strong and weak hypotheses plays an essential role in conducting successful research.
Importance of a Testable Hypothesis
A hypothesis that can be subjected to testing forms the basis of a scientific experiment, outlining anticipated results. For a hypothesis to qualify as testable, it must possess key attributes such as being able to be falsified and verifiable or disprovable via experimental means. It serves as an essential platform for conducting fresh research with the potential to confirm or debunk it.
Crafting a robust testable hypothesis yields clear forecasts derived from previous studies. Should both the predictions and outcomes stemming from a hypothesis lack this critical aspect of testability, they will remain ambiguous, rendering the associated experiment ineffective in conclusively proving or negating anything of substance.
In summary, crafting a strong hypothesis constitutes an essential ability within the realm of scientific research. Grasping the various forms of hypotheses and mastering the process for their formulation and refinement are critical to establishing your research as solid and significant. It is crucial to underscore that having a testable hypothesis serves as the bedrock for successful scientific investigation.
Frequently Asked Questions
How can you formulate a hypothesis.
To formulate a hypothesis, first state the question your experiment aims to answer and identify the independent and dependent variables.
Then create an “If, Then” statement that succinctly defines the relationship between these variables.
What is a hypothesis in scientific research?
In the research process, a hypothesis acts as a tentative concept that is put forward for additional scrutiny and examination, establishing the bedrock upon which scientific experiments are built. It steers the course of research by forecasting possible results.
What are the different types of hypotheses?
Hypotheses can be classified into simple, complex, null, and alternative types, each type fulfilling distinct roles in scientific research.
Understanding these differences is crucial for effective hypothesis formulation.
How do I write a hypothesis?
To write a hypothesis, start by formulating a research question and conducting preliminary research.
Then define your variables and express your hypothesis in the form of an if-then statement.
Why is a testable hypothesis important?
Having a testable hypothesis is vital because it provides a definitive structure for conducting research, allowing for particular predictions that experimentation can either verify or refute.
Such an element significantly improves the process of scientific investigation.
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What Are the Main Qualities of a Good Hypothesis?
A hypothesis is a fundamental element in the scientific method, guiding researchers in their quest for knowledge. A well-crafted hypothesis serves as the foundation for scientific investigations, influencing experimental design and interpretation of results. In this article, we delve into the main qualities that define a good hypothesis.
1. Clarity and Precision
A good hypothesis should be clear and precise, leaving no room for ambiguity. It must state the expected relationship between variables in a straightforward manner, ensuring that anyone reading it can understand the hypothesis without confusion. Precision is crucial, as it helps to define the scope of the study and guide researchers in their data collection and analysis.
2. Testability
A key characteristic of a good hypothesis is its testability. This means that the hypothesis should be formulated in a way that allows it to be empirically tested through observation or experimentation. A hypothesis that cannot be tested is not scientifically meaningful. Testability ensures that the research process is objective and can contribute to the accumulation of reliable scientific knowledge.
3. Falsifiability
Closely related to testability is the concept of falsifiability. A good hypothesis should be framed in a way that it can be proven false. This may seem counterintuitive, but the ability to be proven wrong is a strength of a hypothesis. It distinguishes scientific hypotheses from unfalsifiable statements and pseudoscience. The scientific method relies on the potential for hypotheses to be rejected based on empirical evidence.
4. Relevance to the Research Question
A good hypothesis is directly related to the research question being investigated. It should address the specific issue or phenomenon under study and provide a clear connection between the variables involved. Irrelevant or off-topic hypotheses can lead to misguided research efforts and misinterpretation of results. Ensuring relevance is crucial for maintaining the focus of the investigation.
5. Parsimony
Parsimony, or simplicity, is a quality that suggests a good hypothesis should be as simple as possible while still accounting for the observed phenomena. The principle of parsimony, also known as Occam’s razor, favors the simplest explanation that fits the evidence. A concise hypothesis is easier to test and more likely to be accurate, contributing to the efficiency and effectiveness of the research process.
6. Logical Consistency
Logical consistency is essential in the formulation of a hypothesis. The components of the hypothesis should align logically, and the hypothesis should not contain internal contradictions. A logically consistent hypothesis enhances the credibility of the research and facilitates a more accurate interpretation of the results.
7. Specificity
A good hypothesis is specific and clearly defines the variables and the expected relationship between them. Vague or overly broad hypotheses can lead to difficulties in testing and may result in inconclusive findings. Specificity ensures that the research has a clear direction and allows for a more precise analysis of the data.
In conclusion, a good hypothesis is a cornerstone of scientific inquiry, guiding researchers toward meaningful discoveries. Clarity, testability, falsifiability, relevance, parsimony, logical consistency, and specificity are key qualities that distinguish a well-formulated hypothesis. Researchers must carefully consider these qualities to ensure that their hypotheses contribute meaningfully to the scientific knowledge base.
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How to Write a Great Hypothesis
Hypothesis Definition, Format, Examples, and Tips
Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk, "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.
Verywell / Alex Dos Diaz
- The Scientific Method
Hypothesis Format
Falsifiability of a hypothesis.
- Operationalization
Hypothesis Types
Hypotheses examples.
- Collecting Data
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.
Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."
At a Glance
A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.
The Hypothesis in the Scientific Method
In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:
- Forming a question
- Performing background research
- Creating a hypothesis
- Designing an experiment
- Collecting data
- Analyzing the results
- Drawing conclusions
- Communicating the results
The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.
Unless you are creating an exploratory study, your hypothesis should always explain what you expect to happen.
In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.
Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.
In many cases, researchers may find that the results of an experiment do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.
In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."
In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."
Elements of a Good Hypothesis
So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:
- Is your hypothesis based on your research on a topic?
- Can your hypothesis be tested?
- Does your hypothesis include independent and dependent variables?
Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the journal articles you read . Many authors will suggest questions that still need to be explored.
How to Formulate a Good Hypothesis
To form a hypothesis, you should take these steps:
- Collect as many observations about a topic or problem as you can.
- Evaluate these observations and look for possible causes of the problem.
- Create a list of possible explanations that you might want to explore.
- After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.
In the scientific method , falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.
Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that if something was false, then it is possible to demonstrate that it is false.
One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.
The Importance of Operational Definitions
A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.
Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.
For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.
These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.
Replicability
One of the basic principles of any type of scientific research is that the results must be replicable.
Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.
Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.
To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.
Hypothesis Checklist
- Does your hypothesis focus on something that you can actually test?
- Does your hypothesis include both an independent and dependent variable?
- Can you manipulate the variables?
- Can your hypothesis be tested without violating ethical standards?
The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:
- Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
- Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
- Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
- Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
- Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
- Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.
A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the dependent variable if you change the independent variable .
The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."
A few examples of simple hypotheses:
- "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
- "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."
- "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
- "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."
Examples of a complex hypothesis include:
- "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
- "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."
Examples of a null hypothesis include:
- "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
- "There is no difference in scores on a memory recall task between children and adults."
- "There is no difference in aggression levels between children who play first-person shooter games and those who do not."
Examples of an alternative hypothesis:
- "People who take St. John's wort supplements will have less anxiety than those who do not."
- "Adults will perform better on a memory task than children."
- "Children who play first-person shooter games will show higher levels of aggression than children who do not."
Collecting Data on Your Hypothesis
Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.
Descriptive Research Methods
Descriptive research such as case studies , naturalistic observations , and surveys are often used when conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.
Once a researcher has collected data using descriptive methods, a correlational study can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.
Experimental Research Methods
Experimental methods are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).
Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually cause another to change.
The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.
Thompson WH, Skau S. On the scope of scientific hypotheses . R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607
Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:]. Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z
Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004
Nosek BA, Errington TM. What is replication ? PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691
Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies . Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18
Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.
By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
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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
Systematic Literature Review or Literature Review?
How to Write an Effective Problem Statement for Your Research Paper
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What Are the Elements of a Good Hypothesis?
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A hypothesis is an educated guess or prediction of what will happen. In science, a hypothesis proposes a relationship between factors called variables. A good hypothesis relates an independent variable and a dependent variable. The effect on the dependent variable depends on or is determined by what happens when you change the independent variable . While you could consider any prediction of an outcome to be a type of hypothesis, a good hypothesis is one you can test using the scientific method. In other words, you want to propose a hypothesis to use as the basis for an experiment.
Cause and Effect or 'If, Then' Relationships
A good experimental hypothesis can be written as an if, then statement to establish cause and effect on the variables. If you make a change to the independent variable, then the dependent variable will respond. Here's an example of a hypothesis:
If you increase the duration of light, (then) corn plants will grow more each day.
The hypothesis establishes two variables, length of light exposure, and the rate of plant growth. An experiment could be designed to test whether the rate of growth depends on the duration of light. The duration of light is the independent variable, which you can control in an experiment . The rate of plant growth is the dependent variable, which you can measure and record as data in an experiment.
Key Points of Hypothesis
When you have an idea for a hypothesis, it may help to write it out in several different ways. Review your choices and select a hypothesis that accurately describes what you are testing.
- Does the hypothesis relate an independent and dependent variable? Can you identify the variables?
- Can you test the hypothesis? In other words, could you design an experiment that would allow you to establish or disprove a relationship between the variables?
- Would your experiment be safe and ethical?
- Is there a simpler or more precise way to state the hypothesis? If so, rewrite it.
What If the Hypothesis Is Incorrect?
It's not wrong or bad if the hypothesis is not supported or is incorrect. Actually, this outcome may tell you more about a relationship between the variables than if the hypothesis is supported. You may intentionally write your hypothesis as a null hypothesis or no-difference hypothesis to establish a relationship between the variables.
For example, the hypothesis:
The rate of corn plant growth does not depend on the duration of light.
This can be tested by exposing corn plants to different length "days" and measuring the rate of plant growth. A statistical test can be applied to measure how well the data support the hypothesis. If the hypothesis is not supported, then you have evidence of a relationship between the variables. It's easier to establish cause and effect by testing whether "no effect" is found. Alternatively, if the null hypothesis is supported, then you have shown the variables are not related. Either way, your experiment is a success.
Need more examples of how to write a hypothesis ? Here you go:
- If you turn out all the lights, you will fall asleep faster. (Think: How would you test it?)
- If you drop different objects, they will fall at the same rate.
- If you eat only fast food, then you will gain weight.
- If you use cruise control, then your car will get better gas mileage.
- If you apply a top coat, then your manicure will last longer.
- If you turn the lights on and off rapidly, then the bulb will burn out faster.
- What Is a Testable Hypothesis?
- What Is a Hypothesis? (Science)
- What Are Examples of a Hypothesis?
- Scientific Hypothesis Examples
- Six Steps of the Scientific Method
- Scientific Method Flow Chart
- Null Hypothesis Examples
- Understanding Simple vs Controlled Experiments
- Scientific Method Vocabulary Terms
- Scientific Variable
- What Is an Experimental Constant?
- What Is a Controlled Experiment?
- What Is the Difference Between a Control Variable and Control Group?
- DRY MIX Experiment Variables Acronym
- Random Error vs. Systematic Error
- The Role of a Controlled Variable in an Experiment
2.4 Developing a Hypothesis
Learning objectives.
- Distinguish between a theory and a hypothesis.
- Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
- Understand the characteristics of a good hypothesis.
Theories and Hypotheses
Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.
Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.
A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.
Theories and hypotheses always have this if-then relationship. “ If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.
But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.
Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.
Theory Testing
The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.2 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.
Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.
As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).
Incorporating Theory into Your Research
When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.
To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.
Characteristics of a Good Hypothesis
There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.
Key Takeaways
- A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
- Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
- Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
- Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
- Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
- Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
- Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵
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Learn how to formulate a good hypothesis for your research project with five key characteristics: clarity, testability, relevance, prediction, and existing knowledge. Find out the types of hypotheses, how to test them, and what makes them falsifiable.
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; 5.
4 Alternative hypothesis. An alternative hypothesis, abbreviated as H 1 or H A, is used in conjunction with a null hypothesis. It states the opposite of the null hypothesis, so that one and only one must be true. Examples: Plants grow better with bottled water than tap water. Professional psychics win the lottery more than other people. 5 ...
A well-constructed hypothesis is distinct, precise, and capable of being empirically verified. To be considered a good hypothesis, it must offer measurable and examinable criteria through experimental means. Take the claim “Working from home boosts job satisfaction” as an example. This posits a testable outcome related to work environments.
Conclusion. In conclusion, a good hypothesis is a cornerstone of scientific inquiry, guiding researchers toward meaningful discoveries. Clarity, testability, falsifiability, relevance, parsimony, logical consistency, and specificity are key qualities that distinguish a well-formulated hypothesis. Researchers must carefully consider these ...
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 ...
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 ...
A hypothesis is an educated guess or prediction of what will happen. In science, a hypothesis proposes a relationship between factors called variables. A good hypothesis relates an independent variable and a dependent variable. The effect on the dependent variable depends on or is determined by what happens when you change the independent variable.
How to Write a Good Hypothesis. Writing a good hypothesis is definitely a good skill to have in scientific research. But it is also one that you can definitely learn with some practice if you don’t already have it. Just keep in mind that the hypothesis is what sets the stage for the entire investigation. It guides the methods and analysis.
First, a good hypothesis must be testable and falsifiable. We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical.