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How to Formulate a Hypothesis: Example and Explanation

A hypothesis is a smart guess about how things work. It helps scientists figure out what they think will happen in their experiments. Making a good hypothesis is important because it guides the research and helps find answers to questions. In this article, we will learn how to make a strong hypothesis, look at some examples, and understand why they matter.

Key Takeaways

• A hypothesis is an educated guess that can be tested through experiments.
• Good hypotheses are clear, precise, and can be proven wrong.
• There are different types of hypotheses, like simple, complex, null, and alternative.
• Variables play a big role in forming a hypothesis, including independent, dependent, and control variables.
• Testing and refining hypotheses are crucial steps in scientific research.

Understanding the Concept of a Hypothesis

Definition and importance.

A hypothesis is an idea you can test. It's a clear statement predicting the outcome of your study. It's not just a guess ; it should be based on what you already know. A good hypothesis helps you focus your research and guides your experiments.

Role in Scientific Research

In science, a hypothesis is very important. It gives you a starting point for your experiments. You can test it to see if it's true or false. This helps you understand more about the world. A clear, testable hypothesis is key to good research .

Common Misconceptions

Many people think a hypothesis is just a wild guess. This is not true. A hypothesis is based on existing knowledge and theories. Another common mistake is making the hypothesis too broad. A good hypothesis should be specific and testable.

Steps to Formulate a Hypothesis

Formulating a hypothesis is a critical step in the scientific method. It involves several key stages that help ensure your hypothesis is both testable and relevant to your research question. Here are the steps you should follow:

Gathering Observations

Start by collecting as many observations about your topic or problem as possible. These observations will form the foundation of your hypothesis. Good clinical research starts from a plausible hypothesis supported by contemporary scientific knowledge. Look for patterns or trends in the data that might suggest a possible explanation.

Identifying Variables

Next, identify the variables involved in your study. Variables are the elements that you will measure or manipulate in your research. There are typically three types of variables: independent, dependent, and control variables. Understanding these will help you design a more effective experiment.

Developing Possible Explanations

Once you have gathered your observations and identified your variables, the next step is to develop possible explanations for the patterns you have observed. This is where you start to formulate your hypothesis. Think of ways to confirm or disprove each possible explanation through experimentation. This process is known as falsifiability and is crucial for a robust hypothesis.

Characteristics of a Good Hypothesis

Testability and falsifiability.

A good hypothesis must be testable, meaning you can design an experiment to check if it's true or false. Testability is crucial because it allows you to gather evidence to support or refute your hypothesis. Additionally, a hypothesis should be falsifiable, which means there should be a possible outcome that can prove it wrong. This aligns with the falsification principle proposed by Karl Popper, which is fundamental in scientific research.

Clarity and Precision

Your hypothesis should be clear and precise, avoiding any vague language. This clarity helps in demystifying the concept of a thesis statement . A well-defined hypothesis makes it easier to design experiments and interpret results. For example, instead of saying "Plants grow better with more light," you could say, "If plants receive 8 hours of sunlight daily, then they will grow taller than plants that receive 4 hours of sunlight daily."

Relevance to Research Question

A good hypothesis should be directly related to your research question. It should provide a clear direction for your study and help you focus on specific variables. This relevance ensures that your hypothesis is not just a random guess but is grounded in existing knowledge and observations. Hypotheses have strong, arguably foundational, utility as a tool of science . They support the falsification principle, proposed by Karl Popper as fundamental in scientific research.

Types of Hypotheses in Research

When conducting research, it's crucial to understand the different types of hypotheses you might encounter. Each type serves a unique purpose and helps guide your study in specific ways. Knowing these types can enhance the clarity and focus of your research proposal .

Examples of Hypotheses

Simple hypothesis examples.

A simple hypothesis suggests a relationship between two variables: one independent and one dependent. For instance, "If students sleep for at least 8 hours, then their test scores will improve." This type of hypothesis is straightforward and easy to test.

Complex Hypothesis Examples

A complex hypothesis involves more than two variables. An example could be, "If students sleep for at least 8 hours and eat a healthy breakfast, then their test scores and overall well-being will improve." This type of hypothesis examines multiple factors and their combined effects.

Null and Alternative Hypothesis Examples

The null hypothesis states that there is no relationship between the variables. For example, "There is no difference in test scores between students who sleep for 8 hours and those who do not." The alternative hypothesis, on the other hand, suggests a relationship: "Students who sleep for 8 hours will have better test scores than those who do not."

Understanding these examples helps clarify how to structure your own hypotheses. Whether simple or complex, each type plays a crucial role in scientific research.

The Role of Variables in Hypothesis Formulation

When formulating a hypothesis, understanding the role of variables is crucial. Variables are the elements that you measure or manipulate in your research . They help you establish relationships and test your predictions effectively.

Testing Your Hypothesis

Designing experiments.

Designing an experiment is a crucial step in testing your hypothesis. A well-designed experiment ensures that you can accurately test your hypothesis and obtain reliable results. Start by defining your independent and dependent variables clearly. Make sure to control other factors that might influence the outcome. This is essential for maintaining the integrity of your experiment. You should also consider the ethical implications of your experiment to ensure it adheres to accepted standards.

Data Collection Methods

Once your experiment is designed, the next step is to collect data. Choose data collection methods that are appropriate for your research question and hypothesis. Common methods include surveys, observations, and experiments. Ensure that your data collection process is systematic and consistent to avoid any biases. Remember, the goal is to gather data that will either support or refute your hypothesis.

Analyzing Results

After collecting your data, the next step is to analyze the results. Use statistical methods to determine whether your data supports your hypothesis. This involves calculating the likelihood that your results are due to chance. If your data does not support your hypothesis, don't be discouraged. Unexpected findings can lead to new questions and further research. Always be open to conducting further experiments to validate and understand your findings.

Common Pitfalls in Hypothesis Formulation

When formulating a hypothesis, it's crucial to avoid common mistakes that can undermine your research. Here are some pitfalls to watch out for:

Overly Broad Hypotheses

One of the most frequent errors is creating a hypothesis that is too broad. A broad hypothesis can be difficult to test and may not provide meaningful results. Narrowing down your hypothesis to a specific aspect of your research question can make it more manageable and testable.

Lack of Testability

A hypothesis must be testable to be valid. If you can't design an experiment to test your hypothesis, it's not useful. Ensure that your hypothesis includes variables that can be measured and tested. This is essential for revolutionizing research: the secrets of effective experimental design .

Ignoring Alternative Explanations

Another common mistake is failing to consider other possible explanations for your observations. When you ignore alternative explanations, you risk missing out on important insights. Always evaluate assumptions, revise methodology, and consider alternative explanations to strengthen your hypothesis.

By being aware of these pitfalls, you can create a more robust and reliable hypothesis for your research.

Refining and Revising Hypotheses

When you conduct research, it’s common to find that your initial hypothesis may not hold true. This is a normal part of the scientific process. If your results do not support your original hypothesis, consider suggesting alternative options for future studies. This can help guide further research and improve understanding of the topic.

To ensure your hypothesis is strong, you can use a checklist to identify any weaknesses. Here are some questions to consider:

• Is the hypothesis clear and specific?
• Can it be tested through experiments?
• Does it relate to the research question?

By answering these questions, you can refine your hypothesis and make it more robust. Additionally, incorporating feedback from peers can provide new insights and help you adjust your hypothesis based on new data.

In summary, refining and revising your hypothesis is essential for advancing your research. It allows you to adapt to new findings and improve the clarity and focus of your work. Remember, the goal is to develop a hypothesis that can lead to meaningful conclusions and further exploration in your field.

In the context of educational research, a recent meta-analysis highlights the importance of understanding the relationship between psychological needs and student well-being. This shows how refining hypotheses can lead to better insights into complex issues. Similarly, a grounded theory study emphasizes the need for thorough reviews to identify key issues in research, which can also inform hypothesis revision.

Case Studies of Hypothesis Formulation

One of the most famous historical examples of hypothesis formulation is Gregor Mendel's work on pea plants. Mendel's hypothesis about inheritance patterns laid the groundwork for modern genetics. He observed the traits of pea plants and formulated hypotheses about how these traits were passed down through generations. His work is a classic example of how careful observation and hypothesis testing can lead to significant scientific breakthroughs.

In contemporary research, hypothesis formulation continues to play a crucial role. For instance, in the field of psychology, researchers often develop hypotheses to understand human behavior. A recent study on the effects of social media on mental health formulated the hypothesis that increased social media use leads to higher levels of anxiety and depression. This hypothesis was tested through surveys and data analysis, providing valuable insights into the relationship between social media and mental health.

From both historical and contemporary examples, several lessons can be learned about effective hypothesis formulation:

• Observation is key : Careful observation of phenomena is the first step in formulating a hypothesis.
• Clarity and precision : A good hypothesis should be clear and precise, making it easier to test.
• Testability: Ensure that your hypothesis can be tested through experiments or data analysis.
• Flexibility: Be prepared to revise your hypothesis based on new data or feedback.

By understanding these lessons, you can improve your own hypothesis formulation process and contribute to the advancement of scientific knowledge.

In our "Case Studies of Hypothesis Formulation" section, we dive into real-world examples that show how to create strong hypotheses. These case studies are designed to help you understand the process and apply it to your own work. If you're looking for more detailed guidance, visit our website for step-by-step instructions and special offers. Don't miss out on the chance to improve your research skills!

Formulating a hypothesis is a fundamental step in the scientific method that helps guide research and experimentation. By gathering observations, evaluating potential causes, and developing testable statements, researchers can create hypotheses that are both meaningful and falsifiable. This process not only aids in understanding the problem at hand but also in predicting outcomes and drawing conclusions based on empirical evidence. Remember, a well-crafted hypothesis is clear, concise, and provides a direction for future research. With practice and careful consideration, anyone can learn to formulate effective hypotheses that contribute to scientific knowledge.

Frequently Asked Questions

What is a hypothesis.

A hypothesis is an educated guess about how things work. It's a statement that can be tested to see if it's true or false.

Why is a hypothesis important in scientific research?

A hypothesis helps guide your experiments and research. It gives you a clear focus and helps you understand what you're trying to find out.

What are the steps to formulate a good hypothesis?

To create a good hypothesis, start by gathering observations, look for patterns, and identify variables. Then, come up with possible explanations that you can test.

What makes a hypothesis testable?

A testable hypothesis is one that you can prove or disprove through experiments or observations. It should be clear and specific.

Can a hypothesis be proven true?

A hypothesis can be supported by evidence, but it can't be proven true beyond all doubt. New evidence might change our understanding.

What are independent and dependent variables?

Independent variables are the ones you change in an experiment. Dependent variables are the ones you measure to see if they change because of the independent variable.

What is a null hypothesis?

A null hypothesis states that there is no relationship between the variables being studied. It's often used as a starting point for testing.

How can I avoid common pitfalls in hypothesis formulation?

To avoid problems, make sure your hypothesis is specific, testable, and based on observations. Avoid making it too broad or ignoring other possible explanations.

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• How to Write a Strong Hypothesis | Guide & Examples

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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McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 16 September 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

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

• 4 minute read
• 376.6K views

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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How to Write a Hypothesis [31 Tips + Examples]

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

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

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

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

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

Writing a Hypothesis: The Basics

Table of Contents

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

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

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

31 Tips for Writing a Hypothesis

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

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

Tip 1: Start with a Clear Research Question

A clear research question ensures your hypothesis is targeted.

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

Tip 2: Do Background Research

Research helps you understand current knowledge and any existing gaps.

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

Tip 3: Identify Independent and Dependent Variables

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

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

Tip 4: Make Your Hypothesis Testable

A hypothesis must be measurable and falsifiable.

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

Tip 5: Be Specific and Concise

Keep your hypothesis straightforward and to the point.

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

Tip 6: Choose Simple Language

Use simple, understandable language to ensure clarity.

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

Tip 7: Formulate a Null Hypothesis

A null hypothesis assumes no relationship between variables.

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

Tip 8: State Alternative Hypotheses

Provide alternative hypotheses to explore other plausible relationships.

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

Tip 9: Use “If…Then” Statements

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

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

Tip 10: Avoid Assumptions

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

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

Tip 11: Review Existing Literature

Previous research offers insights into forming a hypothesis.

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

Tip 12: Consider Multiple Variables

Hypotheses with multiple variables can offer deeper insights.

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

Tip 13: Review Ethical Considerations

Ethics are essential for trustworthy research.

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

Tip 14: Test with Pilot Studies

Small-scale pilot studies test feasibility and refine hypotheses.

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

Tip 15: Build Hypotheses on Existing Theories

Existing theories provide strong foundations.

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

Tip 16: Address Real-World Problems

Solve real-world problems through practical hypotheses.

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

Tip 17: Aim for Clear, Measurable Outcomes

The results should be easy to measure and interpret.

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

Tip 18: Stay Open to Unexpected Results

Not all hypotheses yield expected results.

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

Tip 19: Keep Hypotheses Relevant

Ensure your hypothesis aligns with the purpose of your research.

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

Tip 20: Collaborate with Peers

Collaboration strengthens hypothesis development.

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

Tip 21: Re-evaluate Hypotheses Periodically

Revising hypotheses ensures relevance.

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

Tip 22: Develop Compelling Visuals

Illustrating hypotheses can help communicate relationships effectively.

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

Tip 23: Refine Your Data Collection Plan

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

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

Tip 24: Focus on Logical Progression

Ensure your hypothesis logically follows your research question.

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

Tip 25: Test Against Diverse Samples

Testing across diverse samples ensures broader applicability.

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

Tip 26: Use Control Groups

Control groups provide a baseline for comparison.

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

Tip 27: Consider Practical Constraints

Work within realistic constraints for your resources and timeline.

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

Tip 28: Recognize Bias Risks

Biases can skew hypothesis formation.

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

Tip 29: Prepare for Peer Review

Peer review ensures your hypothesis holds up to scrutiny.

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

Tip 30: Create a Research Proposal

A proposal outlines your hypothesis, methodology, and significance.

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

Tip 31: Document Your Findings

Recording findings helps validate or challenge your hypothesis.

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

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

Hypothesis Examples for Different Situations

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

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

Professional Contacts

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

Super Personal

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

Final Thoughts: How to Write a Hypothesis

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

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

Stay curious and let your research journey unfold.

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How To Write a Strong Research Hypothesis

Are you looking to take your research project to the next level? Have you heard of the power of a hypothesis but need to figure out how to formulate one that will unlock potential discoveries? We can help!

So get ready; it's time to dive into unlocking the power of research! This blog post will explore what makes a well-crafted and powerful hypothesis - from identifying a research question to developing supporting evidence.

By learning how to craft a compelling hypothesis, you'll have more tremendous success in every step of your research project.

What are hypotheses, and why are they important?

A hypothesis is an educated guess or a proposition based on limited evidence as a starting point for further investigation. It provides a framework for research and allows researchers to refine their ideas, collect data, and draw conclusions. Hypotheses are essential to the process because they will enable us to organize our thoughts and test theories properly.

Hypotheses are used in many fields , from medicine to psychology to economics. In each area, developing hypotheses based on observations enable researchers to make predictions about their data and guide them toward finding meaningful results.

For example, in medicine, hypotheses can be used to predict which treatments will be most effective for particular conditions or which drugs may have adverse effects when taken together. This allows doctors to make better decisions when caring for patients.

In psychology, hypotheses are often used in experiments to determine whether certain variables influence behavior or mental processes. By testing different combinations of variables, psychologists can identify patterns and understand why people behave the way they do.

In economics, hypotheses provide economists with a framework for analyzing the relationship between economic variables such as wages and consumer spending habits. By understanding these relationships, economists can better understand how economic forces affect the economy.

Overall, hypotheses play an essential role in helping scientists develop new ideas and draw meaningful conclusions from the collected data. Without taking the step to create hypotheses, it would be difficult for researchers to make sense of the vast amounts of information available today and use it effectively in their investigations.

How to determine an effective research question to form your hypothesis

When conducting research, having a compelling research question is critical . Properly formulating this question will allow the researcher to develop their hypothesis. A research question provides a clear and focused goal for your research study and also gives direction on how to get there. A compelling research question should be specific, answerable in the context of your field of study, significant, novel (not already answered by previous studies), and timely – that is, relevant to current events or trends.

Before determining the best research question, you must first understand your topic. Think about the area of knowledge that interests you most and narrow it down to a single theme or concept within this topic. Focus on what interests you most within this theme, and make sure there is room for further exploration and analysis. Once you have chosen a specific topic and narrowed down your focus, you can begin formulating questions related to your project.

To ensure relevance and impact to your field of study, choose questions that address essential issues in the literature or suggest solutions to existing problems. Avoid overly broad topics with unclear objectives; instead, opt for focused questions to enable targeted data collection and analysis with concrete results.

Additionally, consider time frames when formulating questions. If the issue has been discussed extensively in the past but has not been revisited recently, then it's likely not worthy of a new investigation.

Once you have developed some potential questions related to your topic, review them carefully and decide which question best captures the essence of what you want to learn through researching this topic.

Ask yourself:

• Is this question answerable?
• Does it fit within my field of study?
• Is it significant enough?
• Would its findings be novel?

If so, then congratulations! You have identified a compelling research question.

Tips for crafting a well-crafted hypothesis

Once you have formulated the official research question, you may develop the formal hypothesis. When composing a hypothesis, it's essential to think carefully about the question you are trying to answer.

A solid hypothesis should be testable, meaning that it can be verified or disproved through research. It should also be specific and focused on one issue at a time. Here are some tips for crafting a well-crafted hypothesis:

• Consider the goal of your research: Think about what it is that you want to learn or determine from your experiment and make sure that your hypothesis reflects this goal.
• Create an educated guess as to why something is happening: Your hypothesis should explain why something is occurring based on what evidence you already have and direct further investigation into the matter. For example, if you hypothesize that increased carbon dioxide levels in the atmosphere will lead to global warming, your research should focus on examining this relationship further.
• Define any variables or parameters involved in the experiment: This includes things like temperature or chemical composition that could potentially affect the outcome of any experiments done in pursuit of testing your hypothesis.
• Use clear and precise language: Make sure your hypothesis is written with clear and precise language so that anyone reading it can understand exactly what you are attempting to investigate or explain. Avoid complex words and keep sentences short whenever possible.

Following these simple tips will help ensure that your hypothesis is well-crafted and ready for testing!

Examples of evidence that can support your hypothesis

When it comes to developing a hypothesis, supporting evidence is essential for making sure it holds up. This evidence helps strengthen the argument that is being driven by providing facts and logical reasoning that support the hypothesis.

Examples of evidence that can be used to back up a hypothesis include using data from experiments, case studies, and other research projects. Data from experiments can provide insight into how certain variables interact to form a particular outcome.

Case studies may offer greater depth in understanding a specific phenomenon's cause and effect; research projects may yield results that confirm or refute existing theories on a subject.

In addition to these traditional forms of evidence, personal experiences or observations can also help to support a hypothesis. For example, if someone's daily commute has been consistently faster since they changed routes, they could use their personal experience to argue that making this change resulted in shorter commutes.

Similarly, suppose someone has witnessed how two variables consistently coincide (i.e., when one goes up, another goes down). In that case, this could be used to support the notion that there is some correlation between these two aspects.

Overall, evidence to support your hypothesis is crucial for ensuring its validity and credibility. While conducting experiments or researching may seem like time-consuming processes, having solid supporting evidence will make it much easier to defend your ideas convincingly when challenged.

Therefore, it is crucial to take the time necessary to gather credible sources of information to provide the most substantial possible backing for your hypotheses.

Understanding the potential of hypotheses and how they can help your research project progress

The power of research lies in the ability to develop and test hypotheses. A hypothesis is a statement or an idea that can be tested to determine its validity.

Essentially, it is a form of educated guesswork that helps researchers form conclusions about their data. By developing a hypothesis for a research project, you are effectively setting up the framework for further exploration.

When developing a hypothesis, you must consider both the expected outcomes and possible alternative explanations. This will help you focus on testing the possible results without getting sidetracked by irrelevant information. Once you have established a concrete hypothesis, it can then be used as a basis for further research and experimentation.

The process of testing hypotheses is an integral part of the scientific method and can help researchers build confidence in their findings and conclusions. Through careful observation and experimentation, researchers can compare their results against what they initially hypothesized, allowing them to draw more accurate conclusions about their data. As such, hypotheses play an essential role in helping researchers connect the dots between different pieces of evidence and form meaningful conclusions.

Overall, understanding how hypotheses can be used in research projects can be immensely beneficial in helping progress towards reaching meaningful insights from their data. By setting up expectations ahead of time and then testing them against real-world conditions, researchers can gain valuable insights that could potentially change the way we understand our world – now that's something worth exploring!

Final thoughts

A hypothesis is a proposed explanation for an observable phenomenon. It's important to note that hypotheses are not the same thing as theories–a theory is a much broader and well-established frame of reference that explains multiple phenomena.

Generally, scientists form a research question and then narrow it down to a testable hypothesis. After making observations and conducting experiments to gather data, researchers can use evidence to support or reject the hypothesis.

By following these steps to formulate a solid hypothesis, you will be on your way to developing a successful research project. Happy researching!

Header image by Bnenin .

Instant insights, infinite possibilities

How to write a research hypothesis

Last updated

19 January 2023

Reviewed by

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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How to Write a Research Hypothesis

• Research Process
• Peer Review

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 Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

This is awesome.Wow.

It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

Explained well and in simple terms. Quick read! Thank you

It awesome. It has really positioned me in my research project

Brief and easily digested

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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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5 EASY Steps to Write a Comprehensive Hypothesis

by  Antony W

August 1, 2024

This is the most comprehensive guide on how to write a hypothesis. In this lesson, we will tell you everything you everything you need to know to develop a good research hypothesis for your project.

In particular, you will learn what a hypothesis is, what makes a good hypothesis, types of hypothesis, and the steps you can take to come up with a solid hypothesis for your research project.

What is a Hypothesis?

A hypothesis is a scientific method that introduces a specific research question in a scientific experiment and then proposes a testable prediction about the expected results in a study.

What Makes a Good Hypothesis?  

Since a small error in your scientific method can undermine your experiment, it’s important to be very careful when developing your research hypothesis.

A reasonable first approach is to understand what makes a good hypothesis before you start developing one.

In particular, you want to make sure your hypothesis meets the following conditions, with an exception of none.

• Your research hypothesis should be in a clear and focused language
• It must be testable, with a clear description of the testing methods
• The hypothesis must have a possible explanation, which allows for further exploration
• It should have a reasonable relationship with your research topic
• This scientific method should include dependent as well as independent variables
• It should allow for alterations without going against the ethical standards of the development of research methods

The reason why your hypothesis must meet the above conditions is that it helps to build your research method on a solid foundation.

Moreover, these characteristics even make it easier for you to identify gaps and weaknesses in your hypothesis, so that you can make changes and end up with a more promising and testable hypothesis.

Types of Hypothesis

There are seven types of hypothesis that you can develop for your research project. We discuss all of them below to give you more insight so you can understand them.

Simple Hypothesis

Sometimes referred to as a basic hypothesis, a simple hypothesis gives a prediction between an independent and a dependent variable. In this case, the independent variable is a cause and the dependent is the effect.

For example, when a researcher states that “Global warming causes iceberg to melt”, global warming is the cause and melting iceberg is the effect.

Complex Hypothesis

A complex hypothesis is different from a simple hypothesis in that it gives a prediction between two or more independent and dependent variables.

Directional Hypothesis

A directional hypothesis gives a researcher the direction they should follow to determine the relationship between independent and dependent variables.

Often derived from a theory, this hypothesis shows the intellectual commitment of a researcher to a given possible outcome. Moreover, the relationship between the variables can also predict the nature of the hypothesis.

Non-directional Hypothesis

A non-directional hypothesis doesn’t show the exact nature of the relationship between independent and dependent variables.

It’s useful in cases where there are no theories involved, or when evaluating previous researches that were contradictory in form.

Associative and Causal Hypothesis

An associative hypothesis defines the interdependency between variables. Usually, a change in on variable will lead to a change in another.

A causal hypothesis, on the other hand, suggests an effect on the dependent variable arising from the manipulation of the independent variables.

Null Hypothesis

A null hypothesis gives a negative statement that supports a researcher’s finding. Usually, there’s no relationship between an independent and a dependent variable.

  Alternative Hypothesis

This hypothesis shows an existing relationship between two variables and indicates that the results are necessary to the research topic in question.

How to Write a Hypothesis for Your Research

Credit: CrazyEgg

Below is a step-by-step guide to help you write a good hypothesis for your upcoming research project:

1. Ask the Right Question 

The first step to developing a hypothesis is to come up with a research question that you would like to answer. Your research question is the problem you’re trying to solve.

So it should be specific and easy to research within the constraints of the project.

2. Conduct Your Research 

Spend some time researching your topic to discover what researchers already know about the topic.

Examine previous studies and theories and use your findings to develop reasonable assumptions. Also identify both dependent and independent variables that you will study.

Independent variables are the ones that you can change, control, or manipulate. They’re often in isolation from other factors of the study.

Dependent variables are not in isolation from other factors of the study. Also, a change in the independent variables can easily influence them.

3. Develop Your Hypothesis 

Your preliminary research should give you some ideas that you can use to develop your hypothesis.

Use these ideas to write a clear answer to the research question that you came up with in step one.

4. Refine Your Hypothesis

It’s important to make sure that your hypothesis meets the criteria we discussed. So examine it to determine if it’s specific and testable.

Check whether the independent and dependent variables are relevant. Identify the study group. Then, determine the right prediction of the analysis or experiment.

Identifying your variables shouldn’t be difficult. As you saw in our “Global warming causes iceberg to melt” example of hypothesis , the first part of the sentence is the independent variable and the second is the independent.

You can also phrase your hypothesis in the form of correlation. In this case, you have to predict the relationship between one or more variables.

In the case where you’ve decided to compare two groups of independent variables, you should give clear differences in the results that you expect to find.

5. Formulate a Null Hypothesis 

Instructors expect you to come up with a null hypothesis in the case where your research involves statistical testing.

A null hypothesis will be your default position where you show that there isn’t an existing relationship between independent and dependent variables.

About the author 

Antony W is a professional writer and coach at Help for Assessment. He spends countless hours every day researching and writing great content filled with expert advice on how to write engaging essays, research papers, and assignments.

Educational resources and simple solutions for your research journey

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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Chapter 2 formulating a hypothesis.

“There is no single best way to develop a research idea.” ( Pischke 2012 )

2.1 How do you develop a research question and formulate a hypothesis?

You decide to undertake a scientific project. Where do you start? First, you need to find a research question that interests you and formulate a hypothesis. We will introduce some key terminology, steps you can take, and examples how to develop research questions. Note that .

What if someone assigns a topic to me? For students attending undergraduate and graduate courses that often pick topics from a list, all of these steps are equally important and necessary. You still need to formulate a research question and a hypothesis. And it is important to clarify the relevance of your topic for yourself.

When thinking about a research question, you need to identify a topic that is:

• Relevant , important in the world and interesting to you as a researcher: Does working on the topic excites you? You will spend many hours thinking about it and working on it. Therefore, it should be interesting and engaging enough for you to motivate your continued work on this topic.
• Specific : not too broad and not too narrow
• Feasible to research within a given time frame: Is it possible to answer the research question based on your time budget, data and additional resources.

How do you find a topic or develop a feasible research idea in the first place? Finding an idea is not difficult, the critical part is to find a good idea. How do you do that? There is no one specific way how one gets an idea, rather there is a myriad of ways how people come up with potential ideas (for example, as stated by Varian ( 2016 ) ).

You can find inspiration by

• Looking at insights from the world around you: your own life and experiences, observe the behavior of people around you
• Talking to people around you, experts, other students, family members
• Talking to individuals outside your field (non-economists)
• Talking to professionals working in the area you are interested in (you may use social media and professional platforms like LinkedIN or Twitter to make contact)
• Reading journal articles from other non-economic social sciences and the medical literature
• What are the issues being discussed?
• How do these issues affect people’s lives?

In addition you could

• Go to virtual and in-person seminars, for example, the Essen Health Economics Seminar
• Look at abstracts of scientific articles and working papers
• Look at the literature in a specific field you are interested in, for example, screening complete issues of journals or editorials about certain research advancements. By reading this literature you might come up with the idea on how to extend and refine previous research.

Once you identified a research question that is of interest to you, you need to define a hypothesis.

2.2 What is a hypothesis?

A hypothesis is a statement that introduces your research question and suggests the results you might find. It is an educated guess. You start by posing an economic question and formulate a hypothesis about this question. Then you test it with your data and empirical analysis and either accept or reject the hypothesis. It constitutes the main basis of your scientific investigation and you should be careful when creating it.

2.2.1 Develop a hypothesis

Before you formulate your hypothesis, read up on the topic of interest. This should provide you with sufficient information to narrow down your research question. Once you find your question you need to develop a hypothesis, which contains a statement of your expectations regarding your research question’s results. You propose to prove your hypothesis with your research by testing the relationship between two variables of interest. Thus, a hypothesis should be testable with the data at hand. There are two types of hypotheses: alternative or null. Null states that there is no effect. Alternative states that there is an effect.

There is an alternative view on this that suggests one should not look at the literature too early on in the idea-generating process to not be influenced and shaped by someone else’s ideas ( Varian 2016 ) . According to this view you can spend some time (i.e. a few weeks) trying to develop your own original idea. Even if you end up with an idea that has already been pursued by someone else, this will still provide you with good practice in developing publishable ideas. After you have developed an idea and made sure that it was not yet investigated in the literature, you can start conducting a systematic literature review. By doing this, you can find some other interesting insights from the work of others that you can synthesize in your own work to produce something novel and original.

2.2.2 Identify relevant literature

For your research project you will need to identify and collect previous relevant literature. It should involve a thorough search of the keywords in relevant databases and journals. Place emphasis on articles from high-ranking journals with significant numbers of citations. This will give you an indication of the most influential and important work in the field. Once you identify and collect the relevant literature for your topic, you will need to critically synthesize it in your literature review.

When you perform your literature review, consider theories that may inform your research question. For example, when studying physician behavior you may consider principal-agent theory.

2.2.3 Research question or literature review: the chicken or the egg problem?

Whether you start reading the literature first or by developing an idea may depend on your level (graduate student, early career researcher) and other goals. However, thinking freely about what you like to investigate first may help to critically develop a feasible and interesting research question.

We highlight an example how to start with investigating the real world and subsequently posing a research question ( “How to Write a Strong Hypothesis Steps and Examples ” 2019 ; “Developing Strong Research Questions Criteria and Examples ” 2019 ; Schilbach 2019 ) . For example, based on your observation you notice that people spend extensive amount of time looking at their smartphones. Maybe even you yourself engage in the same behavior. In addition, you read a BBC News article Social media damages teenagers’ mental health, report says .

(#fig:social_media)Social media and mental health

Source: BBC

You decide to translate this article and your observations into a research question : How does social media use affect mental health? Before you formulate your hypothesis, read up on the topic of interest. Read economic, medical and other social science literature on the topic. There is likely to be a vast amount of literature from non-economic fields that are doing research on your topic of interest, for example, psychology or neuroscience. Familiarize yourself with it and master it. Do not get distracted by different scientific methodologies and techniques that might seem not up-to-par to the economic studies (small sample sizes, endogeneity, uncovering association rather than causation, etc.), but rather focus on suggestions of potential mechanisms.

A hypothesis is then your research question distilled into a one sentence statement, which presents your expectations regarding the results. You propose to prove your hypothesis by testing the relationship between two variables of interest with the data at hand. There are two types of hypotheses: alternative or null. The null hypothesis states that there is no effect. The alternative hypothesis states that there is an effect.

A hypothesis related to the above-stated research question could be: The increased use of social media among teenagers leads to (is associated with) worse mental health outcomes, i.e. increased incidence of depression, eating disorders, worse well-being and lower self-esteem. It suggests a direction of a relationship that you expect to find that is guided by your observations and existing evidence. It is testable with scientific research methods by using statistical analysis of the relevant data.

Your hypothesis suggests a relationship between two variables: social media use (your independent variable \(X\) ) and mental health (dependent variable \(Y\) ). It could be framed in terms of correlation (is associated with) or causation (leads to). This should be reflected in the choice of scientific investigation you decide to undertake.

The null hypothesis is: There is no relationship between social media use among teenagers and their mental health .

2.3 Resources box

2.3.1 how to develop strong research questions.

• The form of the research process
• Varian, H. R. (2016). How to build an economic model in your spare time. The American Economist, 61(1), 81-90.

2.3.2 Identify relevant literature from major general interest and field literature

To identify the relevant literature you can

• use academic search engines such as Google Scholar, Web of Science, EconLit, PubMed.
• search working paper series such as the National Bureau of Economic Research , NetEc or IZA
• search more general resource sites such as Resources for Economists
• go to the library/use library database

2.3.3 Assess the quality of a journal article

Several rankings may help to assess the quality of research you consider

• Journals of general interest and by field in economics and management - For German-speaking countries, consider the VWL / BWL Handelsblatt Ranking for economics and management - The German Association of Management Scholars provides an expert-based ranking VHB JourQual 3.0, Teilranking Management im Gesundheitswesen - Web of Science Impact Factors - Scimago
• Health Economics, Health Services and Health Care Managment Research: Health Economics Journals List
• Be aware that like in any other domain there are predatory publishing practices .

Use tools to investigate how a journal article is connected to other works

• Citationgecko
• Connected papers
• scite_ – a tool to get a first impression whether a study is disputed or academic consensus

2.3.4 Organize your literature

• Zotero (free of charge)
• Mendeley (free of charge)
• EndNote (potentially free of charge via your university)
• Citavi (potentially free of charge via your university)
• BibTEX if you work with TEX
• Excel spread sheet

2.4 Checklist to get started with formulating your hypothesis

• Find an interesting and relevant research topic, if not assigned
• Try to suck up all information you can easily obtain from various sources within and outside academic literature
• Formulate one compelling research question
• Find the best available empirical and theoretical evidence that is related to your research question
• Formulate a hypothesis
• Check whether data are available for analysis
• Challenge your idea with your fellows or senior researchers

2.5 Example: Hellerstein ( 1998 )

As an illustration of the research process of formulating a hypothesis, designing a study, running a study, collecting and analyzing the data and, finally, reporting the study, we provide an example by replicating Judith K. Hellerstein’s paper “The Importance of the Physician in the Generic versus Trade-Name Prescription Decision” that was published in 1998 in the RAND Journal of Economics.

Hellerstein’s 1998 paper has impacted discussion about behavioral factors of physician decisions and pharmaceutical markets over two decades. The study received 448 citations on Google Scholar since 1998 by 27/03/2022, including recent mentions in top field journals such as Journal of Public Economics (2021) , Journal of Health Economics (2019) , and Health Economics (2019) .

Figure 2.1: Connected graph of Hellerstein ( 1998 ) , February 2022

Figure 2.1 shows a connected graph of prior and derivative works related to the study.

The work has impacted the literature researching the role of physician behavior and its influence on access, adoption and diffusion of health services, moral hazard and incentives in prescription and treatment decisions and the influence of different payment schemes, and a vast body of literature studying the pharmaceutical market.

The research that has been influenced by Hellerstein includes evidence on:

• generic drug entries and market efficiency
• the effectiveness of pharmaceutical promotion
• the effectiveness of price regulations
• the role of patents and dynamics of market segmentation

At the end of each chapter, we demonstrate insights into this study that we replicate.

2.5.1 Context of the study - escalating health expenditures

In the United States, the total prescription drug expenditure in 2020 marked about 358.7 billion US Dollars ( Statista n.d. ) . The prescription of generic drugs in comparison to more expensive brand-name versions is an option in reducing the total health care expenditure. Generic drugs are bioequivalent in the active ingredients and can serve as a channel to contain prescription expenditure ( Kesselheim 2008 ) as generic drugs are between 20 and 90% cheaper than their trade-name alternatives ( Dunne et al. 2013 ) .

2.5.2 Research question - How does a patient’s insurance status influence the physician’s choice between generic compared to brand-name drugs?

Physicians are faced with a multitude of medication options, including the choice between generic and trade-name drugs. Physicians ideally act as agents for their patients to identify the best available treatment option based on their needs. Choosing the best treatment entails cost of coordination and cognition. The prescription of generic drugs may serve as an example to what extent physicians customize treatments according to patients’ needs with regards to cost. From an economic point of view we may expect that once a generic drug is available, a perfectly rational agent (i.e. physician) would prescribe a generic drug instead of the trade-name version if therapeutically identical ( Dranove 1989 ) . This leads to the following research question: “Do physicians vary their prescription decisions on a patient-by-patient basis or do they systematically prescribe the same version, trade-name or generic, to all patients?” .

The 1998 Hellerstein’s study examines two hypotheses:

• The physician prescribing choice influences the selection of a generic over a brand-name drug
• The patient’s insurance status influences the physician’s choice between generic and brand-name drugs.

For the purpose of this example and in the replication exercise we focus on the second aspect.

2.5.3 Hypothesis

The paper formulates the following hypothesis:

Physicians are more likely to prescribe generics to patients who do not have insurance coverage for prescription pharmaceuticals (moral hazard in insurance)

Hellerstein ( 1998 ) discusses that, based on insurance status, some patients may demand certain care more than others. If, for example, the prescription drug is reimbursed by the patient’s health insurance, this may cause overconsumption. This behavior can potentially differ by the patient’s insurance scheme. A patient that has no insurance and, thus, does not get any reimbursement for prescription drugs, might have a higher incentive to demand cheaper generic drugs ( Danzon and Furukawa 2011 ) than a patient with insurance that covers prescription drugs, either generic or trade-name. Given that the United States have different insurance schemes with varying prescription drug coverage, it is of interest to investigate the role of a patient’s insurance status in the physician’s choice between generic compared to brand-name drugs.

Hellerstein ( 1998 ) considers a patient’s insurance status as a matter of dividing the study population in groups for which the choice between generic and brand-name drugs differs. She suggests that There is a relationship between the prescription of a generic drug and insurance status of a patient. ( Hellerstein 1998 ) .

Providing answers to a research question requires formulating and testing a hypothesis. Based on logic, theory or previous research, a hypothesis proposes an expected relationship within the given data. According to her research question, Hellerstein hypothesizes that: Physicians are more likely to prescribe generics to patients who do not have insurance coverage for prescription pharmaceuticals.

Specifically, she writes “if there is moral hazard in insurance when it comes to physician prescription behavior, there will be differences in the propensity of physicians to prescribe low-cost generic drugs, and these differences will be (partially) a function of the insurance held by the patient. In particular, if moral hazard exists, patients with extensive insurance coverage for prescription drugs (like those on Medicaid in 1989) should receive prescriptions written for generic drugs less frequently than patients with no prescription drug coverage.” ( Hellerstein 1998, 113 )

Based on Hellerstein’s considerations, we expect the effect of the insurance status on whether a patient receives a generic to be different from zero. To obtain a testable null hypothesis, we reformulate this relationship so that we reject the hypothesis if our expectations are correct. This means, if we expect to see an effect of insurance on prescriptions of generics, our null hypothesis is that insurance status has no effect on the outcome (prescription of generic drugs). No moral hazard arises from having obtained insurance.

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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."

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

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

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

There are 5 main steps in hypothesis testing:

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

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

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

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

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

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

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Scientific Method: Step 3: HYPOTHESIS

• Step 1: QUESTION
• Step 2: RESEARCH
• Step 3: HYPOTHESIS
• Step 4: EXPERIMENT
• Step 5: DATA
• Step 6: CONCLUSION

Step 3: State your hypothesis

Now it's time to state your hypothesis . The hypothesis is an educated guess as to what will happen during your experiment. 

The hypothesis is often written using the words "IF" and "THEN." For example, " If I do not study, then I will fail the test." The "if' and "then" statements reflect your independent and dependent variables . 

The hypothesis should relate back to your original question and must be testable .

A word about variables...

Your experiment will include variables to measure and to explain any cause and effect. Below you will find some useful links describing the different types of variables.

• "What are independent and dependent variables" NCES
• [VIDEO] Biology: Independent vs. Dependent Variables (Nucleus Medical Media) Video explaining independent and dependent variables, with examples.

Resource Links

• What is and How to Write a Good Hypothesis in Research? (Elsevier)
• Hypothesis brochure from Penn State/Berks

• << Previous: Step 2: RESEARCH
• Next: Step 4: EXPERIMENT >>
• Last Updated: Aug 26, 2024 10:04 AM
• URL: https://harford.libguides.com/scientific_method

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• Research Paper >

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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Browse Full Outline

• 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
• 5.4 Methods
• 5.5 Results
• 5.6 Discussion
• 5.7 Conclusion
• 5.8 Bibliography
• 6.1 Table of Contents
• 6.2 Acknowledgements
• 6.3 Appendix
• 7.1 In Text Citations
• 7.2 Footnotes
• 7.3.1 Floating Blocks
• 7.4 Example of a Paper
• 7.5 Example of a Paper 2
• 7.6.1 Citations
• 7.7.1 Writing Style
• 7.7.2 Citations
• 8.1.1 Sham Peer Review
• 8.1.2 Advantages
• 8.1.3 Disadvantages
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• 8.3.1 Journal Rejection
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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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Martyn Shuttleworth (Aug 1, 2009). How to Write a Hypothesis. Retrieved Sep 17, 2024 from Explorable.com: https://explorable.com/how-to-write-a-hypothesis

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