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How to Write a Hypothesis: Step-By-Step Guide

A hypothesis is a testable statement that guides scientific research. Want to know how to write a hypothesis for your research paper? This guide will show you the key steps involved, including defining your variables and phrasing your hypothesis correctly.

Key Takeaways

• A hypothesis is a testable statement proposed for investigation, grounded in existing knowledge, essential for guiding scientific research.
• Understanding different types of hypotheses, including simple, complex, null, and alternative, is crucial for selecting appropriate research approaches.
• Crafting a strong hypothesis involves a systematic process including defining variables, phrasing it as an if-then statement, and ensuring it is clear, specific, and testable.

Understanding a Hypothesis

An empirical hypothesis is not just a simple guess. It represents a preliminary concept that stands to be scrutinized through Research and experimentation. A well-constructed hypothesis is a fundamental component of the scientific method, guiding experiments and leading to conclusions. Within the realm of science, such hypotheses are crafted after an extensive examination of current knowledge, ensuring their foundation on already established evidence prior to beginning any new inquiry.

Essentially, a hypothesis in the scientific community must present itself as something capable of being tested, this characteristic distinguishes it from mere speculation by allowing its potential verification or falsification through methodical scrutiny. Hypotheses serve as crucial instruments within scientific studies, directing these investigations toward particular queries and forming the backbone upon which all experiments rest in their pursuit for advancements in comprehension.

When formulating a hypothesis for testing within research activities, one should employ language that remains neutral and detached from subjective bias thereby bolstering the legitimacy of outcomes produced during the study. This precision fosters greater confidence in results obtained under rigorous evaluation standards among peers.

Characteristics of a Good Hypothesis

A good hypothesis is the cornerstone of any successful scientific research. It should be clear, concise, and testable, providing a solid foundation for your investigation. Here are some key characteristics that define a good hypothesis:

• Clarity : A good hypothesis should be easy to understand and clearly state the expected outcome of the research. For example , “Increased exposure to sunlight will result in taller plant growth” is a clear and straightforward hypothesis.
• Conciseness : Avoid unnecessary complexity or jargon. A concise hypothesis is brief and to the point, making it easier to test and analyze. For instance, “Exercise improves mental health” is concise and direct.
• Testability : A good hypothesis must be testable and falsifiable, meaning it can be proven or disproven through scientific research methods. For example, “Consuming vitamin C reduces the duration of the common cold” is a testable hypothesis.
• Relevance : Ensure your hypothesis is relevant to the research question or problem and aligned with your research objectives. For example, if your research question is about the impact of diet on health, a relevant hypothesis could be “A high-fiber diet reduces the risk of heart disease.”
• Specificity : A good hypothesis should be specific and focused on a particular aspect of the research question. For example, “Daily meditation reduces stress levels in college students” is specific and targeted.
• Measurability : Your hypothesis should be measurable, meaning it can be quantified or observed. For example, “Regular physical activity lowers blood pressure” is a measurable hypothesis.

By ensuring your hypothesis possesses these characteristics, you set a strong foundation for your scientific research, guiding your investigation towards meaningful and reliable results.

Types of Hypotheses

Scientific research incorporates a range of research hypotheses, which are crucial for proposing relationships between different variables and steering the direction of the investigation. These seven unique forms of hypotheses cater to diverse needs within the realm of scientific inquiry.

Comprehending these various types is essential in selecting an appropriate method for conducting research. To delve into details, we have simple, complex, null and alternative hypotheses. Each brings its distinct features and practical implications to the table. It underscores why recognizing how they diverge and what purposes they serve is fundamental in any scientific study.

Simple Hypothesis

A basic hypothesis suggests a fundamental relationship between two elements: the independent and dependent variable. Take, for example, a hypothesis that says, “The taller growth of plants (dependent variable) is due to increased exposure to sunlight (independent variable).” Such hypotheses are clear-cut and easily testable as they concentrate on one direct cause-and-effect link.

These types of straightforward hypotheses are very beneficial in scientific experiments because they permit the isolation of variables for precise outcome measurement. Their simplicity lends itself well to being an essential component in conducting scientific research, thanks to their unambiguous nature and targeted focus on specific relationships.

Complex Hypothesis

Alternatively, a complex hypothesis proposes an interconnection amongst several variables. It builds on the concept of numerous variable interactions within research parameters. Take for instance a causal hypothesis which asserts that sustained alcohol consumption (the independent variable) leads to liver impairment (the dependent variable), with additional influences like use duration and general health results impacting this relationship.

Involving various factors, complex hypotheses reveal the nuanced interaction of elements that affect results. Although they provide extensive insight into studied phenomena, such hypotheses necessitate advanced research frameworks and analysis techniques to be understood properly.

Null Hypothesis

In the realm of hypothesis testing, the null hypothesis (H0) serves as a fundamental presumption suggesting that there exists no association between the variables under investigation. It posits that variations within the dependent variable are attributed to random chance and not an influential relationship. Take for instance a null hypothesis which could propose “There is no impact of sleep duration on productivity levels.”

The significance of the null hypothesis lies in its role as a reference point which researchers strive to refute during their investigations. Upon uncovering statistical evidence indicative of a substantial linkage, it becomes necessary to discard the null hypothesis. The act of rejecting this foundational assumption is critical for affirming research findings and assessing their importance with respect to outcomes observed.

Alternative Hypothesis

The alternative hypothesis, often represented by H1 or Ha, contradicts the null hypothesis and proposes a meaningful link between variables under examination. For example, where the null hypothesis asserts that a particular medication is ineffective, the alternative might posit that “Compared to placebo treatment, the new drug yields beneficial effects.”

By claiming outcomes are non-random and carry weight, the alternative hypothesis bolsters theoretical assertions. Its testable prediction propels scientific investigation forward as it aims either to corroborate or debunk what’s posited by the null hypothesis.

Consider an assertive statement like “Productivity is influenced by sleep duration” which serves as a crisp articulation of an alternative hypothesis.

Steps to Write a Hypothesis

Crafting a hypothesis is a methodical process that begins with curiosity and culminates in a testable prediction. Writing a hypothesis involves following structured steps to ensure clarity, focus, and researchability. Steps include asking a research question, conducting preliminary research, defining variables, and phrasing the hypothesis as an if-then statement.

Each step is critical in formulating a strong hypothesis to guide research and lead to meaningful discoveries.

Ask a Research Question

A well-defined research question forms the cornerstone of a strong hypothesis, guiding your investigation towards a significant and targeted exploration. By rooting this question in observations and existing studies, it becomes pertinent and ripe for research. For example, noting that certain snacks are more popular could prompt the inquiry: “Does providing healthy snack options in an office setting enhance employee productivity?”.

Such a thoughtfully constructed question lays the groundwork for your research hypothesis, steering your scholarly work to be concentrated and purposeful.

Conduct Preliminary Research

Begin your research endeavor by conducting preliminary investigations into established theories, past studies, and available data. This initial stage is crucial as it equips you with a comprehensive background to craft an informed hypothesis while pinpointing any existing voids in current knowledge. Understanding the concept of a statistical hypothesis can also be beneficial, as it involves drawing conclusions about a population based on a sample and applying statistical evidence.

By reviewing literature and examining previously published research papers, one can discern the various variables of interest and their interconnections. Should the findings from these early inquiries refute your original hypothesis, adjust it accordingly so that it resonates with already recognized evidence.

Define Your Variables

A well-formed hypothesis should unambiguously identify the independent and dependent variables involved. In an investigation exploring how plant growth is affected by sunlight, for instance, plant height represents the dependent variable, while the quantity of sunlight exposure constitutes the independent variable.

It is essential to explicitly state all the variables included in a study so that the hypothesis can be tested with accuracy and specificity. Defining these variables distinctly facilitates a targeted and quantifiable examination.

Phrase as an If-Then Statement

A good hypothesis is typically structured in the form of if-then statements, allowing for a clear demonstration of the anticipated link between different variables. Take, for example, stating that administering drug X could result in reduced fatigue among patients. This outcome would be especially advantageous to individuals receiving cancer therapy. The structure aids in explicitly defining the cause-and-effect dynamic.

In order to craft a strong hypothesis, it should be capable of being tested and grounded on existing knowledge or theoretical frameworks. It should also be framed as a statement that can potentially be refuted by experimental data, which qualifies it as a solidly formulated hypothesis.

Collect Data to Support Your Hypothesis

Once you have formulated a hypothesis, the next crucial step is to collect data to support or refute it. This involves designing and conducting experiments or studies that test the hypothesis, and collecting and analyzing data to determine whether the hypothesis holds true.

Here are the key steps in collecting data to support your hypothesis:

• Designing an Experiment or Study : Start by identifying your research question or problem. Design a study or experiment that specifically tests your hypothesis. For example, if your hypothesis is “Daily exercise improves cognitive function,” design an experiment that measures cognitive function in individuals who exercise daily versus those who do not.
• Collecting Data : Gather data through various methods such as experiments, surveys, observations, or other techniques. Ensure your data collection methods are reliable and valid. For instance, use standardized tests to measure cognitive function in your exercise study.
• Analyzing Data : Use statistical methods or other techniques to analyze the data. This step involves determining whether the data supports or refutes your hypothesis. For example, use statistical tests to compare cognitive function scores between the exercise and non-exercise groups .
• Interpreting Results : Interpret the results of your data analysis to determine whether your hypothesis is supported. For instance, if the exercise group shows significantly higher cognitive function scores, your hypothesis is supported. If not, you may need to refine your hypothesis or explore other variables.

By following these steps, you can systematically collect and analyze data to support or refute your hypothesis, ensuring your research is grounded in empirical evidence.

Refining Your Hypothesis

To ensure your hypothesis is precise, comprehensible, verifiable, straightforward, and pertinent, you must refine it meticulously. Creating a compelling hypothesis involves careful consideration of its transparency, purposeful direction and the potential results. This requires unmistakably delineating the subject matter and central point of your experiment.

Your hypothesis should undergo stringent examination to remove any uncertainties and define parameters that guarantee both ethical integrity and scientific credibility. An effective hypothesis not only questions prevailing assumptions, but also maintains an ethically responsible framework.

Testing Your Hypothesis

Having a robust research methodology is essential for efficiently evaluating your hypothesis. It is important to ensure that the integrity and validity of the research are upheld through adherence to ethical standards. The data gathered ought to be both representative and tailored specifically towards validating or invalidating the hypothesis.

In order to ascertain whether there’s any significant difference, statistical analyses measure variations both within and across groups. Frequently, the decision on whether to discard the null hypothesis hinges on establishing a p-value cut-off point, which conventionally stands at 0.05.

Tips for Writing a Research Hypothesis

Writing a research hypothesis can be a challenging task, but with the right approach, you can craft a strong and testable hypothesis. Here are some tips to help you write a research hypothesis:

• Start with a Research Question : A good hypothesis starts with a clear and focused research question. For example, “Does regular exercise improve mental health?” can lead to a hypothesis like “Regular exercise reduces symptoms of depression.”
• Conduct Preliminary Research : Conducting preliminary research helps you identify a knowledge gap in your field and develop a hypothesis that is relevant and testable. Review existing literature and studies to inform your hypothesis.
• Use Clear and Concise Language : A good hypothesis should be easy to understand and use clear and concise language. Avoid jargon and complex terms. For example, “Increased screen time negatively impacts sleep quality” is clear and straightforward.
• Avoid Ambiguity and Vagueness : Ensure your hypothesis is free from ambiguity and vagueness. Clearly state the expected outcome of the research. For example, “Consuming caffeine before bedtime reduces sleep duration” is specific and unambiguous.
• Make Sure It Is Testable : A good hypothesis should be testable and falsifiable, meaning it can be proven or disproven through scientific research methods. For example, “A high-protein diet increases muscle mass” is a testable hypothesis.
• Use Existing Knowledge and Research : Base your hypothesis on existing knowledge and research. Align it with your research objectives and ensure it is grounded in established theories or findings.

Common mistakes to avoid when writing a research hypothesis include:

• Making It Too Broad or Too Narrow : A good hypothesis should be specific and focused on a particular aspect of the research question. Avoid overly broad or narrow hypotheses.
• Making It Too Vague or Ambiguous : Ensure your hypothesis is clear and concise, avoiding ambiguity and vagueness.
• Failing to Make It Testable : A good hypothesis should be testable and falsifiable. Ensure it can be proven or disproven through scientific research methods.
• Failing to Use Existing Knowledge and Research : Base your hypothesis on existing knowledge and research. Align it with your research objectives and ensure it is grounded in established theories or findings.

By following these tips and avoiding common mistakes, you can write a strong and testable research hypothesis that will guide your scientific investigation towards meaningful and reliable results.

Examples of Good and Bad Hypotheses

A well-constructed hypothesis is distinct, precise, and capable of being empirically verified. To be considered a good hypothesis, it must offer measurable and examinable criteria through experimental means. Take the claim “Working from home boosts job satisfaction” as an example. This posits a testable outcome related to work environments.

On the other hand, a subpar hypothesis such as “Garlic repels vampires” falls short because it hinges on fantastical elements that cannot be substantiated or refuted in reality. The ability to distinguish between strong and weak hypotheses plays an essential role in conducting successful research.

Importance of a Testable Hypothesis

A hypothesis that can be subjected to testing forms the basis of a scientific experiment, outlining anticipated results. For a hypothesis to qualify as testable, it must possess key attributes such as being able to be falsified and verifiable or disprovable via experimental means. It serves as an essential platform for conducting fresh research with the potential to confirm or debunk it.

Crafting a robust testable hypothesis yields clear forecasts derived from previous studies. Should both the predictions and outcomes stemming from a hypothesis lack this critical aspect of testability, they will remain ambiguous, rendering the associated experiment ineffective in conclusively proving or negating anything of substance.

In summary, crafting a strong hypothesis constitutes an essential ability within the realm of scientific research. Grasping the various forms of hypotheses and mastering the process for their formulation and refinement are critical to establishing your research as solid and significant. It is crucial to underscore that having a testable hypothesis serves as the bedrock for successful scientific investigation.

Frequently Asked Questions

How can you formulate a hypothesis.

To formulate a hypothesis, first state the question your experiment aims to answer and identify the independent and dependent variables.

Then create an “If, Then” statement that succinctly defines the relationship between these variables.

What is a hypothesis in scientific research?

In the research process, a hypothesis acts as a tentative concept that is put forward for additional scrutiny and examination, establishing the bedrock upon which scientific experiments are built. It steers the course of research by forecasting possible results.

What are the different types of hypotheses?

Hypotheses can be classified into simple, complex, null, and alternative types, each type fulfilling distinct roles in scientific research.

Understanding these differences is crucial for effective hypothesis formulation.

How do I write a hypothesis?

To write a hypothesis, start by formulating a research question and conducting preliminary research.

Then define your variables and express your hypothesis in the form of an if-then statement.

Why is a testable hypothesis important?

Having a testable hypothesis is vital because it provides a definitive structure for conducting research, allowing for particular predictions that experimentation can either verify or refute.

Such an element significantly improves the process of scientific investigation.

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How to Write a Hypothesis: Step-by-Step Guide with Examples

A well-crafted hypothesis is the foundation of any successful research project. Knowing how to write a hypothesis can help you focus your study, set clear objectives, and guide your experiments effectively. A hypothesis isn’t just a guess; it’s an informed prediction that you can test through research.

Whether you’re working on a science project or exploring a question in social sciences, creating a strong hypothesis gives your work direction and purpose. In this guide, we’ll explore the steps to formulating a solid hypothesis that’s both testable and meaningful.

Steps to Writing a Strong Hypothesis

Creating a strong hypothesis involves a few key steps to ensure it’s both clear and testable. Here’s a step-by-step guide to help you develop an effective hypothesis for your research.

1. Start with a Research Question

Every hypothesis begins with a research question that addresses what you want to explore or understand. This question should be specific and relevant to your area of study.

For example, instead of a broad question like “Why do plants grow?”, a more focused question would be, “How does sunlight affect the growth rate of tomato plants?” Starting with a precise question lays the foundation for a strong hypothesis.

2. Conduct Preliminary Research

Before formulating a hypothesis, gather some background information on your topic. Review existing studies, theories, or findings that relate to your question.

This research helps you make an educated prediction rather than a random guess, giving your hypothesis a solid foundation. For instance, if you’re studying plant growth, research how sunlight influences other types of plants to understand potential outcomes for tomato plants.

3. Formulate Your Hypothesis as a Statement

A hypothesis should be a clear, concise statement that predicts an outcome. Avoid phrasing it as a question. A well-phrased hypothesis for the previous example might be: “If tomato plants are exposed to more sunlight, then they will grow taller.” This statement directly predicts a relationship between sunlight and plant height, making it easier to test.

4. Identify the Variables

Determine the independent and dependent variables in your hypothesis. The independent variable is the factor you will change or manipulate (in this case, sunlight), while the dependent variable is the outcome you’ll measure (the growth of the plants). Clearly defining these variables keeps your hypothesis focused and measurable.

5. Make It Testable and Specific

A strong hypothesis is testable, meaning it can be supported or refuted through experimentation or observation. Ensure that your statement is specific enough to allow for a straightforward test.

Avoid vague language, such as “sunlight might help plants grow,” and instead opt for direct predictions, like “increased sunlight exposure will result in taller tomato plants.”

6. Predict the Expected Relationship

A hypothesis often includes a prediction about the relationship between variables, whether it’s positive, negative, or neutral.

For example, in the hypothesis, “If tomato plants receive six hours of sunlight daily, they will grow faster than plants receiving only three hours,” you are clearly stating that increased sunlight will positively impact growth.

7. Write a Null Hypothesis (Optional)

In many research settings, especially in scientific experiments, you may also write a null hypothesis. The null hypothesis (often abbreviated as H₀) is a statement that there is no relationship between the variables.

For example, the null hypothesis for the plant study would be, “There is no difference in growth rate between tomato plants receiving varying amounts of sunlight.” This provides a baseline comparison for your main hypothesis.

8. Revise and Refine

Finally, review your hypothesis to ensure it is clear, specific, and testable. Adjust any language that may seem vague or overly complex. A concise, well-phrased hypothesis is easier to work with and interpret, allowing your research to flow more smoothly.

For instance, if your original hypothesis was overly complex, simplify it to something like, “Tomato plants exposed to six hours of sunlight daily will grow taller than those receiving three hours.”

Characteristics of a Good Hypothesis

A strong hypothesis is essential for guiding your research and ensuring your findings are meaningful. Below are the key characteristics that make a hypothesis effective, allowing for a structured and insightful investigation.

1. Clarity and Precision

A good hypothesis is clear, direct, and easy to understand. Avoid vague language or overly complex phrasing that might cause confusion.

For example, instead of saying, “Plants might respond differently to light,” a clear hypothesis would be, “Tomato plants exposed to six hours of sunlight will grow taller than those exposed to three hours.” This clarity helps you and others know exactly what you’re testing and makes the research process smoother.

2. Testability

A hypothesis should be testable through experimentation or observation, meaning you should be able to gather evidence to support or refute it.

For instance, a hypothesis like “Higher levels of sunlight will increase tomato plant growth” can be tested by exposing plants to varying levels of sunlight. Testability is crucial, as it allows you to collect data that directly addresses your hypothesis.

3. Specificity

An effective hypothesis is specific, focusing on a single, measurable outcome. This specificity ensures that you’re not testing too many factors at once, which can complicate the analysis.

For example, “Tomato plants exposed to six hours of sunlight will grow faster than those receiving three hours” is specific because it defines both the conditions (sunlight exposure) and the expected outcome (growth rate).

4. Relevance

A strong hypothesis addresses a question or problem that is relevant to the field of study or to the specific research objective. A hypothesis on sunlight exposure and plant growth, for instance, would be relevant to agricultural studies.

Relevance ensures that your research is meaningful and can contribute valuable insights or advancements to existing knowledge.

5. Simplicity

A good hypothesis is simple and straightforward, avoiding unnecessary complexity. Simplicity makes it easier to conduct and analyze the research.

For instance, a hypothesis like, “Increasing the sunlight exposure from three to six hours daily will increase tomato plant height” is simple, with a clear independent variable (sunlight) and dependent variable (plant height). Simplicity is especially important in experiments, where too many variables can make results hard to interpret.

6. Consistency with Existing Knowledge

While a hypothesis can aim to explore new ideas, it should still align with or logically extend from what is already known. Consistency with existing research adds credibility and helps position your hypothesis within a larger scientific framework.

For instance, if previous studies show that light affects plant growth, your hypothesis on sunlight and growth height would logically build on those findings.

7. Statement of Expected Relationship

A strong hypothesis often states the expected relationship between variables, whether positive, negative, or neutral. For example, a hypothesis that states, “Tomato plants exposed to more sunlight will grow taller than those receiving less sunlight” clearly indicates a positive relationship between sunlight and growth. This expectation helps guide the design of your experiment and establishes a basis for analysis.

8. Objectivity

A strong hypothesis is objective, free from personal bias or assumptions that might influence the outcome. An objective hypothesis is based on observable, measurable variables rather than subjective opinions.

For instance, stating, “Increased sunlight will improve plant growth” is more objective than saying, “Sunlight is better for plants,” as it focuses on measurable outcomes rather than personal beliefs.

Examples of Hypotheses in Different Fields

A well-constructed hypothesis can vary widely depending on the field of study, as each discipline explores different variables and outcomes. Here are some examples of hypotheses across various fields to illustrate how they apply to specific types of research.

1. Psychology

In psychology, hypotheses often focus on understanding behavior, mental processes, and the effects of various factors on human or animal psychology. For instance:

• Hypothesis: “Individuals who practice mindfulness for 10 minutes daily will experience lower levels of anxiety compared to those who do not.”
• Explanation: This hypothesis is testable and specific, predicting a measurable outcome (anxiety levels) based on a specific independent variable (mindfulness practice).

Biological hypotheses frequently address the impact of environmental factors, genetics, or physiology on living organisms. For example:

• Hypothesis: “Tomato plants exposed to eight hours of sunlight will grow taller than plants exposed to four hours of sunlight.”
• Explanation: This hypothesis is clear, specific, and testable. It sets up a direct comparison of two conditions (different levels of sunlight) to measure the dependent variable (plant height).

3. Sociology

In sociology, hypotheses often aim to understand social behavior, cultural influences, or group dynamics. An example might be:

• Hypothesis: “High school students who participate in extracurricular activities will have higher self-esteem than those who do not participate.”
• Explanation: This hypothesis predicts a relationship between two variables: participation in extracurricular activities (independent variable) and self-esteem (dependent variable). It’s relevant, testable, and addresses a social phenomenon.

4. Medicine and Health Sciences

Medical research often includes hypotheses that focus on health outcomes, treatments, or risk factors. For example:

• Hypothesis: “Patients who receive eight hours of sleep per night will recover faster from surgery than those who receive fewer than six hours of sleep.”
• Explanation: This hypothesis examines the impact of sleep duration (independent variable) on recovery speed (dependent variable), which is measurable and relevant to health sciences.

5. Environmental Science

Hypotheses in environmental science commonly address the effects of environmental changes on ecosystems, resources, or species. An example could be:

• Hypothesis: “Increasing nitrogen levels in soil will lead to faster growth rates in grass species.”
• Explanation: This hypothesis predicts a cause-and-effect relationship between nitrogen levels (independent variable) and grass growth rate (dependent variable), which can be tested through controlled experiments.

6. Education

In educational research, hypotheses may explore how different teaching methods, environments, or resources affect learning outcomes. For instance:

• Hypothesis: “Students who use interactive digital learning tools will achieve higher test scores than those who use traditional textbooks.”
• Explanation: This hypothesis compares the effect of two teaching tools (digital vs. traditional) on a measurable outcome (test scores), making it testable and relevant to educational studies.

7. Economics

Economics often involves hypotheses about market behavior, consumer choices, or financial impacts. An example might be:

• Hypothesis: “Increasing the minimum wage will lead to a decrease in employee turnover rates in the retail industry.”
• Explanation: This hypothesis proposes a relationship between two variables—minimum wage levels (independent variable) and turnover rates (dependent variable). It can be tested using data analysis within the retail sector.

In physics, hypotheses commonly test relationships between physical forces, properties, or behaviors under specific conditions. For example:

• Hypothesis: “Increasing the mass of an object will increase the gravitational force acting on it.”
• Explanation: This hypothesis is grounded in physics principles and is testable by measuring the force in relation to object mass, making it both specific and measurable.

Marcie Edelson is the voice behind Ansca Mobile, a blog where she explores diverse topics and shares personal experiences. With a passion for discovery, Marcie offers insights and stories that inspire curiosity and exploration.

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

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Table of Contents

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

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

How to Craft a Research Hypothesis  

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

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

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

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

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

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

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

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

What are the Features of an Effective Hypothesis?  

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

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

Understanding Null and Alternative Hypotheses in Research  

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

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

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

Null Hypothesis:  

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

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

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

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

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

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

References  

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

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• 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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Published November 23, 2021. Updated December 13, 2021.

A hypothesis is a testable statement based on the researcher’s expectation for the outcome of a study or an observed phenomenon. It helps establish a relationship between two or more variables. A hypothesis acts as the objective of research and guides the researcher to structure experiments that would produce accurate and reliable results. In all likelihood, if a hypothesis is proven by repeatable and reproducible experiments, it may become a theory or even a law of nature.

What is a hypothesis?

A research hypothesis is an educated, clear, specific and falsifiable prediction of the possible outcomes of scientific observation. A hypothesis can be considered as the starting point of research, as any research without it is aimless. For a hypothesis to be complete, it should contain three main elements, i.e., two or more variables, a population, and the correlation between the variables. A hypothesis lays out a path for researchers, directing them how exactly the experiment should be designed, the type of data that should be collected, the sample size for the experiment, and how the data analysis should be performed, along with providing a basis to obtain results and validate them.

Observation and prior knowledge are the primary steps to developing a research hypothesis.  For example:

You are watching a race in school and observe the speed with which the winner ran. You may wonder why the winner ran so fast. You may think of a few possibilities which could lead to this result, such as the amount of practice before the race, hours of sleep, or consumption of an energy drink. Since the amount of practice and sleep may almost be constant for all the participants, you may feel the win is because of the consumption of an energy drink. So, you may develop a hypothesis such as “Athletes consuming an energy drink daily perform better.”

Developing a good hypothesis

A hypothesis is important as it helps predict the relationship between two variables, which is essential for conducting your research. In the previous example, the researcher uses the consumption of energy drinks and athlete performance as variables and the athletes as a population while trying to establish the effect of the consumption of an energy drink on the performance of an athlete.

A good hypothesis is central to research for providing reliable and valid results. There are a few points should be kept in mind while formulating your hypothesis. Let’s have a look at them.

1)  Ask a question : The foremost step to developing a hypothesis is asking a question. Identifying a question which you are interested in studying is important. For example:

How can air pollution in a region be reduced?

2) Conceptual nature : A hypothesis should be related to a certain concept. This allows the linking of research questions in a study, collecting data, and performing analysis according to the stated concept. For example:

Regions with a greater percentage of tree cover are likely to be less polluted than regions with lower tree cover.

3) Verbal statement : A hypothesis is phrased as a declaration and never as a question. It is the representation of the researcher’s idea or assumption in words that can be tested. For example:

Bad hypothesis: Does following a healthy diet alter the weight of a person?

Good hypothesis: People who follow a healthy diet stay fit.

4) Falsifiable and testable : A hypothesis should be testable so that experiments can be conducted to make observations that agree or disagree with it. It should be falsifiable so that it can be proven wrong if it is found to be incorrect. For example:

Children who use phones while studying score low marks in their exams.

5) Relationship between two variables : A hypothesis suggests a relationship between two or more variables. An independent variable is controlled by the researcher to look at the effects on other variables, i.e., it is the cause for something to happen. A dependent variable is affected by the independent variable and is observed and measured by the researcher. For example:

Consumption of aerated drinks leads to increased blood sugar levels.

Here, the consumption of aerated drinks is the independent variable. The dependent variable is the sugar level that is affected by the consumption of aerated drinks.

6) Specific and precise : A hypothesis should not be too general or vague as obtaining focused results becomes difficult. Also, a hypothesis should not be too specific as it limits the scope of the study. For example:

General: Eating food leads to weight gain.

Specific: Eating ice cream causes weight gain.

Good hypothesis: Consumption of sugar-rich food causes weight in individuals.

If these factors are paid attention to while structuring your hypothesis, you are sure to formulate a sound hypothesis that will direct your research down the correct path.

Types of hypotheses

The hypothesis can be classified into the following categories:

1) Simple Hypothesis : Simple hypotheses draw a relationship between a single independent variable and a single dependent variable. For example:

Increased hours of studying by students leads to them getting better marks.

Here, the hours of study acts as the independent variable while the obtained marks act as the dependent variable.

2) Complex Hypothesis : A complex hypothesis tends to propose a relationship between two or more independent and dependent variables. For example:

Increased hours of studying and eight hours of sleep by students result in getting better marks by an increased attention span.

3) Directional Hypothesis :  This type of hypothesis predicts the nature of the effect of an independent variable on the dependent variable, thus predicting the direction of the effect. For example:

Students scoring good marks in exams tend to have better jobs than the students who score low marks in exams.

Here both the effect and the direction of the effect are represented in the hypothesis.

4) Non-directional Hypothesis : The null hypothesis states a relationship between two variables but does not state the kind of effect that may exist between them. For example:

Students scoring good marks will have jobs different from students scoring low marks.

5) Null Hypothesis : This is a negative statement contrary to the hypothesis and suggests no relationship between the independent and the dependent variable. It is represented as H o . For example:

H o : There is no relationship between hours of study by a student and the earned marks.

H o : Students scoring good and low marks are likely to get similar jobs.

6) Alternative Hypothesis : An alternative to the null hypothesis, it suggests the difference or effect between two or more variables. It is represented as H 1 . For example:

H 1 : There is a relationship between hours of study by a student and the earned marks.

H 1 : Students scoring good and low marks are likely to get different quality jobs.

How to structure a hypothesis?

A hypothesis should be structured in such a way that it should be simple, clear, and easy to understand, and should represent the intent of the hypothesis. There are a few ways to do this:

1) A hypothesis can be represented as a simple ‘if…then’ statement. While the first part of the statement introduces the independent variable, the latter part brings up the dependent variable. For example:

If the plant is watered, then the plant’s growth will improve.

2) A hypothesis can also be written as a statement correlating two variables, directly predicting the relationship between the two variables. For example:

The more times a plant is watered, the better the growth of the plant will be.

3) Another way of structuring a hypothesis is to compare two groups and state the difference expected to occur between the two groups. For example:

Plants that are watered daily are taller than plants that are watered on alternate days.

Testing a hypothesis

Once you have formulated your hypothesis, the next step is to test it to determine if it is correct or incorrect.  The steps given below help to test a hypothesis:

1) State your research hypothesis in the form of a null hypothesis (H o ) and an alternative hypothesis (H 1 ).

2) Perform appropriate experiments and collect data to test the hypothesis.

3) Analyze the data to see whether the hypothesis is supported or refuted.

4) Interpret the data and present your results.

Key takeaways

• A hypothesis is a testable statement based on the researcher’s expectation of an outcome for observed phenomena that is simple, clear, specific, and focuses on only one issue.
• A hypothesis is the focal point of research and directs the course of the research in terms of data collection, sample size, and data analysis.
• A hypothesis is composed of three main components: two or more variables, a population, and the relationship between the variables. Independent and dependent variables are two kinds of variables used while structuring a hypothesis.
• It should be possible to test the hypothesis by performing experiments and prove it to be correct or incorrect.
• A hypothesis helps in testing theories, investigating activities, explaining social phenomena. Further, while acting as a bridge between theory and investigation, it helps determine the most suitable type of research for a problem and allows for the empirical testing of a relationship between variables. If you are lucky, one of your hypotheses may suggest a theory!

Research Process

For more details, visit these additional research guides .

Understand the Research Process

• Research process
• Research questions
• Operationalization
• Research problem
• Statement of the problem
• Background research
• Research hypothesis
• Generalization

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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How to Write a Hypothesis – Steps & Tips

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

What is a Research Hypothesis?

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

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

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

Research Hypothesis Definition

Not sure what the meaning of the research hypothesis is?

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

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

Variables in Hypothesis

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

“How long a student sleeps affects test scores.”

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

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

Looking for dissertation help?

Researchprospect to the rescue then.

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

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

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

Step 1: Start with a Research Question

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

Example: Does exposure to sunlight affect plant growth?

Step 2: Do Preliminary Research

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

Step 3: Define Variables

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

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

Step 4: Formulate the Hypothesis

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

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

Step 5: Ensure it is Testable

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

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

Step 6: Consider Potential Confounding Variables

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

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

Step 7: Write the Null Hypothesis

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

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

Step 8: Test your Hypothesis

Design an experiment or conduct observations to test your hypothesis.

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

Step 9: Analyse the Results

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

Step 10: Draw Conclusions

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

Three Ways to Phrase a Hypothesis

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

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

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

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

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

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

How to Write a Null Hypothesis

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

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

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

Also see:  Your Dissertation in Education

Hypothesis Examples

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

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

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

How can ResearchProspect Help?

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

Frequently Asked Questions

What are the 5 rules for writing a good hypothesis.

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

What is a hypothesis in simple words?

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

What is the hypothesis and examples?

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

What is the hypothesis in research definition?

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

Why is it called a hypothesis?

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

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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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On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

Writing a Strong Hypothesis Statement

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

What is a hypothesis statement?

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

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

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

What is a hypothesis?

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

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

A strong hypothesis statement should be:

• A prediction of the relationship between two or more variables

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

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

Formulating your hypothesis

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

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

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

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

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

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

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

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

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

Refining your hypothesis

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

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

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

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

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

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

What makes a hypothesis weak?

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

Bad hypothesis 1: Diabetes is caused by witchcraft .

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

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

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

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

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

The null hypothesis and the alternative hypothesis

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

What is the null hypothesis?

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

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

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

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

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

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

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

Step-by-step sample hypothesis statements

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

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

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Start with a clear thesis question

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

Create a null hypothesis

Formulate an alternative hypothesis using the variables you have identified

Make sure your hypothesis clearly posits a relationship between variables

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

What makes a hypothesis strong? +

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

What makes a hypothesis weak? +

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

What is the null hypothesis? +

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

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How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis. 

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with. 

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper. 

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories. 

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1. 

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis. 

Another example for a directional one-tailed alternative hypothesis would be that 

H1: Attending private classes before important exams has a positive effect on performance. 

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder: 

What is it that makes dog owners even happier than cat owners? 

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.  

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being . 

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad hypothesis examples, tips for writing a research hypothesis.

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on… 

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript. 

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On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

Hypothesis Testing

Measuring the consistency between a model and data

C lassical statistics features two primary methods for using a sample of data to make an inference about a more general process. The first is the confidence interval, which expresses the uncertainty in an estimate of a population parameter. The second classical method of generalization is the hypothesis test.

The hypothesis test takes a more active approach to reasoning: it posits a specific explanation for how the data could be generated, then evaluates whether or not the observed data is consistent with that model. The hypothesis test is one of the most common statistical tools in the social and natural sciences, but the reasoning involved can be counter-intuitive. Let’s introduce the logic of a hypothesis test by looking at another criminal case that drew statisticians into the mix.

Example: The United States vs Kristen Gilbert

In 1989, fresh out of nursing school, Kristen Gilbert got a job at the VA Medical Center in Northampton, Massachusetts, not far from where she grew up 1 . Within a few years, she became admired for her skill and competence.

Gilbert’s skill was on display whenever a “code blue” alarm was sounded. This alarm indicates that a patient has gone into cardiac arrest and must be addressed quickly by administering a shot of epinephrine to restart the heart. Gilbert developed for a reputation for her steady hand in these crises.

By the mid-1990s, however, the other nurses started to grow suspicious. There seemed to be a few too many code blues, and a few too many deaths, during Gilbert’s shifts. The staff brought their concerns to the VA administration, who brought in a statistician to evaluate the data.

The data that the VA provided to the statistician contained the number of deaths at the medical center over the previous 10 years, broken out by the three shifts of the days: night, daytime, and evening. As part of the process of exploratory data analysis, the statistician constructed a plot.

This visualization reveals several striking trends. Between 1990 and 1995, there were dramatically more deaths than the years before and after that interval. Within that time span, it was the evening shift that had most of the deaths. The exception is 1990, when the night and daytime shifts had the most deaths.

So when was Gilbert working? She began working in this part of the hospital in March 1990 and stopped working in February 1996. Her shifts throughout that time span? The evening shifts. The one exception was 1990, when she was assigned to work the night shift.

This evidence is compelling in establishing an association between Gilbert and the increase in deaths. When the district attorney brought a case against Gilbert in court, this was the first line of evidence they provided. In a trial, however, there is a high burden of proof.

Could there be an alternative explanation for the trend found in this data?

The role of random chance

Suppose for a moment that the occurrence of deaths at the hospital had nothing to do with Gilbert being on shift. In that case we would expect that the proportion of shifts with a death would be fairly similar when comparing shifts where Gilbert was working and shifts where she was not. But we wouldn’t expect those proportions to be exactly equal. It’s reasonable to think that a slightly higher proportion of Gilbert’s shifts could have had a death just due to random chance, not due to anything malicious on her part.

So just how different were these proportions in the data? The plot above shows data from 1,641 individual shifts, on which three different variables were recorded: the shift number, whether or not there was a death on the shift, and whether or not Gilbert was working that shift.

Here are the first 10 observations.

Using this data frame, we can calculate the sample proportion of shifts where Gilbert was working (257) that had a death (40) and compare them to the sample proportion of shifts where Gilbert was not working (1384) that had a death (34).

\[ \hat{p}_{gilbert} - \hat{p}_{no\_gilbert} = \frac{40}{257} - \frac{34}{1384} = .155 - .024 = .131 \]

A note on notation: it’s common to use \(\hat{p}\) (“p hat”) to indicate that a proportion has been computed from a sample of data.

A difference of .131 seems dramatic, but is that within the bounds of what we might expect just due to chance? One way to address this question is to phrase it as: if in fact the probability of a death on a given shift is independent of whether or not Gilbert is on the shift, what values would we expect for the difference in observed proportions?

We can answer this question by using simulation. To a simulate a world in which deaths are independent of Gilbert, we can

• Shuffle (or permute) the values in the death variable in the data frame to break the link between that variable and the staff variable.
• Calculate the resulting difference in proportion of deaths in each group.

The rationale for shuffling values in one of the columns is that if in fact those two columns are independent of one another, then it was just random chance that led to a value of one variable landing in the same row as the value of the other variable. It could just as well have been a different pairing. Shuffling captures another example of the arbitrary pairings that we could have observed if the two variables were independent of one another 2 .

By repeating steps 1 and 2 many many times, we can build up the full distribution of the values that this difference in proportions could take.

As expected, in a world where these two variables are independent of one another, we would expect a difference in proportions around zero. Sometimes, however, that statistic might reach values of +/- .01 or .02 or rarely .03. In the 500 simulated statistics shown above, however, none of them reached beyond +/- .06.

So if that’s the range of statistics we would expect in a world where random chance is the only mechanism driving the difference in proportions, how does it compare to the world that we actually observed? The statistic that we observed in the data was .131, more than twice the value of the most extreme statistic observed above.

To put that into perspective, we can plot the observed statistic as a vertical line on the same plot.

The method used above shows that the chance of observing a difference of .131 is incredibly unlikely if in fact deaths were independent of Gilbert being on shift. On this point, the statisticians on the case agreed that they could rule out random chance as an explanation for this difference. Something else must have been happening.

Elements of a Hypothesis Test

The logic used by the statisticians in the Gilbert case is an example of a hypothesis test. There are a few key components common to every hypothesis test, so we’ll lay them out one-by-one.

A hypothesis test begins with the assertion of a null hypothesis.

It is common for the null hypothesis to be that nothing interesting is happening or that it is business as usual, a hypothesis that statisticians try to refute with data. In Gilbert case, this could be described as “The occurrence of a death is independence of the presence of Gilbert” or “The probability of death is the same whether or not Gilbert is on shift” or “The difference in the probability of death is zero, when comparing shifts where Gilbert is present to shifts where Gilbert is not present”. Importantly, the null model describes a possible state of the world, therefore the latter two versions are framed in terms of parameters ( \(p\) for proportions) instead of observed statistics ( \(\hat{p}\) ).

The hypothesis that something indeed is going on is usually framed as the alternative hypothesis.

In the Gilbert case, the corresponding alternative hypothesis is that there is “The occurrence of a death is dependent on the presence of Gilbert” or “The probability of death is different whether or not Gilbert is on shift” or “The difference in the probability of death is non-zero , when comparing shifts where Gilbert is present to shifts where Gilbert is not present”

In order to determine whether the observed data is consistent with the null hypothesis, it is necessary to compress the data down into a single statistic.

In Gilbert’s case, a difference in two proportions, \(\hat{p}_1 - \hat{p}_2\) is a natural test statistic and the observed test statistic was .131.

It’s not enough, though, to just compute the observed statistic. We need to know how likely this statistic would be in a world where the null hypothesis is true. This probability is captured in the notion of a p-value.

If the p-value is high, then the data is consistent with the null hypothesis. If the p-value is very low, however, there the statistic that was observed would be very unlikely in a world where the null hypothesis was true. As a consequence, the null hypothesis can be rejected as reasonable model for the data.

The p-value can be estimated using the proportion of statistics from the simulated null distribution that are as or more extreme than the observed statistic. In the simulation for the Gilbert case, there were 0 statistics greater than .131, so the estimated p-value is zero.

What a p-value is not

The p-value has been called the most used as well as the most abused tool in statistics. Here are three common misinterpretations to be wary of.

The p-value is the probability that the null hypothesis is true (FALSE!)

This is one of the most common confusions about p-values. Graphically, a p-value corresponds to the area in the tail of the null distribution that is more extreme than the observed test statistic. That null distribution can only be created if you assume that the null hypothesis is true. The p-value is fundamentally a conditional probability of observing the statistic (or more extreme) given the null hypothesis is true. It is flawed reasoning to start with an assumption that the null hypothesis is true and arrive at a probability of that same assumption.

A very high p-value suggests that the null hypothesis is true (FALSE!)

This interpretation is related to the first one but can lead to particularly wrongheaded decisions. One way to keep your interpretation of a p-value straight is to recall the distinction made in the US court system. A trial proceeds under the assumption that the defendant is innocent. The prosecution presents evidence of guilt. If the evidence is convincing the jury will render a verdict of “guilty”. If the evidence is not-convincing (that is, the p-value is high) then the jury will render a verdict of “not guilty” - not a verdict of “innocent”.

Imagine a setting where the prosecution has presented no evidence at all. That by no means indicates that the defendant is innocent, just that there was insufficient evidence to establish guilt.

The p-value is the probability of the data (FALSE!)

This statement has a semblance of truth to it but is missing an important qualifier. The probability is calculated based on the null distribution, which requires the assumption that the null hypothesis is true. It’s also not quite specific enough. Most often p-values are calculated as probabilities of test statistics, not probabilities of the full data sets.

Another more basic check on your understanding of a p-value: a p-value is a (conditional) probability, therefore it must between a number between 0 and 1. If you ever find yourself computing a p-value of -6 or 3.2, be sure to pause and revisit your calculations!

One test, many variations

The hypothesis testing framework laid out above is far more general than just this particular example from the case of Kristen Gilbert where we computed a difference in proportions and used shuffling (aka permutation) to build the null distribution. Below are just a few different research questions that could be addressed using a hypothesis test.

Pollsters have surveyed a sample of 200 voters ahead of an election to assess their relative support for the Republican and Democratic candidate. The observed difference in those proportions is .02. Is this consistent with the notion of evenly split support for the two candidates, or is one decidedly in the lead?

Brewers have tapped 7 barrels of beer and measured the average level of a compound related to the acidity of the beer as 610 parts per million. The acceptable level for this compound is 500 parts per million. Is this average of 610 consistent with the notion that the average of the whole batch of beer (many hundreds of barrels) is at the acceptable level of this compound?

A random sample of 40 users of a food delivery app were randomly assigned two different versions of a menu where they entered the amount of their tip: one with the tip amount in ascending order, the other in descending order. The average tip amount of those with the menu in ascending order was found to be $3.87 while the average tip of the users in the descending order group was $3.96. Could this difference in averages be explained by chance?

Although the contexts of these problems are very different, as are the types of statistics they’ve calculated, they can still be characterized as a hypothesis test by asking the following questions:

What is the null hypothesis used by the researchers?

What is the value of the observed test statistic?

How did researchers approximate the null distribution?

What was the p-value, what does it tell us and what does it not tell us?

In classical statistics there are two primary tools for assessing the role that random variability plays in the data that you have observed. The first is the confidence interval, which quantifies the amount of uncertainty in a point estimate due to the variability inherent in drawing a small random sample from a population. The second is the hypothesis test, which postings a specific model by which the data could be generated, then assesses the degree to which the observed data is consistent with that model.

The hypothesis test begins with the assertion of a null hypothesis that describes a chance mechanism for generating data. A test statistic is then selected that corresponds to that null hypothesis. From there, the sampling distribution of that statistic under the null hypothesis is approximated through a computational method (such as using permutation, as shown here) or one rooted in probability theory (such as the Central Limit Theorem). The final result of the hypothesis test procedure is the p-value, which is approximated as the proportion of the null distribution that is as or more extreme than the observed test statistic. The p-value measures the consistency between the null hypothesis and the observed test statistic and should be interpreted carefully.

A postscript on the case of Kristen Gilbert. Although the hypothesis test ruled out random chance as the reason for the spike in deaths under her watch, it didn’t rule out other potential causes for that spike. It’s possible, after all, that the nightshifts that Gilbert was working happen to be the time of day when cardiac arrests are more common. For this reason, the statistical evidence was never presented to the jury, but the jury nonetheless found her guilty based on other evidence presented in the trial.

The Ideas in Code

A hypothesis test using permutation can be implemented by introducing one new step into the process used for calculating a bootstrap interval. The key distinction is that in a hypothesis test the researchers puts forth a model for how the data could be generated. That is the role of hypothesize() .

hypothesize()

A function to place before generate() in an infer pipeline where you can specify a null model under which to generate data. The one necessary argument is

• null : the null hypothesis. Options include "independence" and "point" .

The following example implements a permutation test under the null hypothesis that there is no relationship between the body mass of penguins and their

• The output is the original data frame with new information appended to describe what the null hypothesis is for this data set.
• There are other forms of hypothesis tests that you will see involving a "point" null hypothesis. Those require adding additional arguments to hypothesize() .

Calculating an observed statistic

Let’s say for this example you select as your test statistic a difference in means, \(\bar{x}_{female} - \bar{x}_{male}\) . While you can use tools you know - group_by() and summarize() to calculate this statistic, you can also recycle much of the code that you’ll use to build the null distribution with infer .

Calculating the null distribution

To generate a null distribution of the kind of differences in means that you’d observe in a world where body mass had nothing to do with sex, just add the hypothesis with hypothesize() and the generation mechanism with generate() .

• The output data frame has reps rows and 2 columns: one indicating the replicate and the other with the statistic (a difference in means).

visualize()

Once you have a collection of test statistics under the null hypothesis saved as null , it can be useful to visualize that approximation of the null distribution. For that, use the function visualize() .

• visualize() expects a data frame of statistics.
• It is a short cut to creating a particular type of ggplot, so like any ggplot, you can add layers to it with + +.
• shade_p_value() is a function you can add to shade the part of the null distribution that corresponds to the p-value. The first argument is the observed statistic, which we’ve recorded as 100 here to see the behavior of the function. direction is an argument where you specify if you would like to shade values "less than" or "more than" the observed value, or "both" for a two-tailed p-value.

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The technical notion that motivates the use of shuffling is a slightly more general notion than independence called exchangability. The distinction between these two related concepts is a topic in a course in probability. ↩︎

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A paraphyletic ‘Silesauridae' as an alternative hypothesis for the initial radiation of ornithischian dinosaurs

Rodrigo temp müller, maurício silva garcia.

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e-mail: [email protected]

Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.5096096 .

Received 2020 Apr 20; Accepted 2020 Jul 30; Issue date 2020 Aug.

Published by the Royal Society. All rights reserved.

Whereas ornithischian dinosaurs are well known from Jurassic and Cretaceous deposits, deciphering the origin and early evolution of the group remains one of the hardest challenges for palaeontologists. So far, there are no unequivocal records of ornithischians from Triassic beds. Here, we present an alternative evolutionary hypothesis that suggests consideration of traditional ‘silesaurids' as a group of low-diversity clades representing a stem group leading to core ornithischians (i.e. unambiguous ornithischians, such as Heterodontosaurus tucki ). This is particularly interesting because it fills most of the ghost lineages that emerge from the Triassic. Following the present hypothesis, the lineage that encompasses the Jurassic ornithischians evolved from ‘silesaurids' during the Middle to early Late Triassic, while typical ‘silesaurids' shared the land ecosystems with their relatives until the Late Triassic, when the group completely vanished. Therefore, Ornithischia changes from an obscure to a well-documented clade in the Triassic and is represented by records from Gondwana and Laurasia. Furthermore, according to the present hypothesis, Ornithischia was the first group of dinosaurs to adopt an omnivorous/herbivorous diet. However, this behaviour was achieved as a secondary step instead of an ancestral condition for ornithischians, as the earliest member of the clade is a faunivorous taxon. This pattern was subsequently followed by sauropodomorph dinosaurs. Indeed, the present scenario favours the independent acquisition of an herbivorous diet for ornithischians and sauropodomorphs during the Triassic, whereas the previous hypotheses suggested the independent acquisition for sauropodomorphs, ornithischians, and ‘silesaurids'.

Keywords: Dinosauria, Dinosauromorpha, evolution, feeding behaviours, Mesozoic era, phylogenetics

1. Introduction

Discoveries across the world are shedding light on the ancestral anatomy of dinosaurs and related groups [ 1 – 7 ]. By contrast, recently unearthed skeletons revealed peculiar combinations of traits that required the establishment of new phylogenetic interpretations. In response, the traditional phylogenetic relationships of dinosaurs have been challenged [ 4 , 8 ]. Therefore, not all studies agree with the classical dichotomy Saurischia/Ornithischia and also with the inner composition of these clades [ 7 – 9 ]. For instance, silesaurids, which are usually considered as the sister-group to Dinosauria [ 1 ], are considered as ornithischians by some authors [ 4 , 10 ]. Indeed, whereas ornithischian dinosaurs are well known from Jurassic and Cretaceous deposits, the origin and early evolution of the group remains one of the hardest challenges for palaeontologists. So far, there are no unequivocal records of ornithischians from Triassic deposits [ 11 ].

These phylogenetic disputes and scarce record of ornithischians hamper the establishment of a reliable framework. However, information on the origin and early evolution of dinosaurs has improved substantially over the last few years. Fieldwork initiated by several researchers has yielded a large number of new species and fossil material from previously described species, including nearly complete early dinosaurs [ 4 , 7 , 12 ] as well as several dinosaur relatives [ 4 , 6 , 13 , 14 ]. Among these discoveries, a lot of new information was produced regarding silesaurids [ 6 , 14 , 15 ], a group with the potential to explain the obscure origin of Ornithischia (see below) [ 10 ]. However, these new data were not combined into a single dataset. In addition, several characters with putative phylogenetic significance have not been incorporated in major phylogenetic datasets. In the present study, we combine these new data and investigate the phylogenetic information content as it pertains to the evolution of dinosaurs. Additionally, we place emphasis on the controversial relationships between ornithischians and silesaurids.

2. Material and methods

The new morphological dataset combines data from different sources, including those from Cabreira et al . [ 4 ] as well as additional operational taxonomic units (OTUs), characters and modifications from other studies [ 7 , 8 , 16 – 19 ]. The added OTUs are as follows: the two unnamed lagerpetids UFSM 11611 and PVSJ 883, Dromomeron gigas , Kwanasaurus williamparkeri , Lutungutali sitwensis , Technosaurus smalli , Soumyasaurus aenigmaticus , Ignotosaurus fragilis , Fruitadens haagarorum , Echinodon beckelessi , Tianyulong confuciusi , Gnathovorax cabreirai , Nhandumirim waldsangae , Bagualosaurus agudoensis , Macrocollum itaquii , Unaysaurus tolentinoi , Teleocrater rhadinus , Spondylosoma absconditum , Yarasuchus deccanensis and Dongusuchus efremovi . The OTUs were coded based on a first-hand examination, photographs and published literature [ 6 , 7 , 13 , 15 , 19 – 26 ]. Furthermore, we scored additional characters for Dromomeron romeri , Lewisuchus admixtus , Asilisaurus kongwe and Buriolestes schultzi based on previous studies [ 5 , 6 , 9 , 12 ]. Based on previous studies, ‘ Marasuchus lilloensis ' was treated as Lagosuchus talampayensis [ 27 ], and ‘ Pseudolagosuchus major ' was combined with Lewisuchus admixtus [ 5 ]. In addition, we followed the reinterpretations regarding the anatomy of Pisanosaurus mertii by [ 15 ]. Finally, morphological characters that support Ornithoscelida were incorporated following [ 8 ]. The final dataset included 277 morphological characters and 62 OTUs.

A phylogenetic analysis based on equally weighted parsimony was implemented in TNT v. 1.1 [ 28 ]. Characters 4, 13, 18, 25, 63, 82, 84, 87, 89, 109, 142, 166, 174, 175, 184, 186, 190, 201, 203, 205, 209, 212, 225, 235, 236, 239, 250 and 256 were treated as additive (i.e. ordered), whereas the other characters were treated as unordered (see supplementary materials for details). Euparkeria capensis was used to root the most parsimonious trees (MPTs) based on a random addition sequence+tree bisection reconnection, which included 1000 replicates of Wagner trees (with random seed = 0), tree bisection reconnection and branch swapping (holding 20 trees saved per replicate). Topologies retained in overflowed replicates were branch-swapped for MPTs using TBR. The strict consensus tree was generated using all trees recovered in the analysis and all OTUs. Decay indices (Bremer support values) and bootstrap values (1000 replicates) were also calculated with TNT v. 1.1 [ 28 ] (see electronic supplementary material, figure S2). In addition, two constrained analyses were performed adopting the same parameters of the former analysis. The first constrained analysis was conducted to access the required number of extra steps to recover a monophyletic Silesauridae apart from a traditional Ornithischia. This involved treatment of silesaurids and core ornithischians as being two distinct monophyletic groups. Pisanosaurus mertii was set as a floating taxon in the constrained searches. The second constrained analysis was conducted to access the required number of extra steps to recover a monophyletic Ornithoscelida. For this analysis, core ornithischians and core theropods were considered to be monophyletic. Pisanosaurus mertii , Eodromaeus murphi , Chindesaurus briansmalli , Tawa hallae and Daemonosaurus chauliodus were set as floating taxa. Finally, an ancestral state reconstruction of diet for the first topology was performed following the same approach by [ 4 ].

The analysis recovered 36 most parsimonious trees (MPTs) of 985 steps (consistence index = 0.320; retention index = 0.665). Lagosuchus talampayensis is sister to Dinosauria ( figure 1 ), which is composed of a traditional Saurischia/Ornithischia arrangement (see electronic supplementary material for a list of synapomorphies and electronic supplementary material, figure S2 for support values). However, the inner affinities of Ornithischia were unusual. Unlike the traditional placement of silesaurids as sister-group to Dinosauria [ 1 , 8 , 9 , 29 ] or as sister-group to the core ornithischians [ 4 , 7 , 10 ], silesaurids appeared as paraphyletic within Ornithischia. Lewisuchus admixtus is the basalmost member of Ornithischia, and Pisanosaurus mertii is the sister taxon to all traditional ornithischians (e.g. Scutellosaurus lawleri ; Eocursor parvus ; Heterodontosaurus tucki ). Sulcimentisauria [ 6 ] includes all core ornithischians and silesaurids, except by Lewisuchus admixtus , Soumyasaurus aenigmaticus and Asilisaurus kongwe . The basalmost member of the clade is Diodorus scytobrachion . The constrained analysis, assuming a monophyletic Silesauridae and a traditional Ornithischia, resulted 117 000 MPTs of 990 steps each (consistence index = 0.318; retention index = 0.663). Here, silesaurids are the sister-group to Dinosauria, which is recovered in the classical fashion of Saurischia/Ornithischia dichotomy. Pisanosaurus mertii is recovered as an ornithischian, rather than within Silesauridae. The constrained analysis, forcing a monophyletic Ornithoscelida, recovered 7632 MPTs of 1010 steps (consistence index = 0.312; retention index = 0.653). Pisanosaurus mertii nests within Silesauridae, which is recovered as the sister-group to a clade composed by Saltopus elginensis plus Dinosauria.

Time-calibrated strict consensus tree depicting the phylogenetic position of traditional ‘silesaurids' with emphasis on the dental characters evolution within ‘Silesauridae'. Numbers on nodes represent Bremer support values higher than 1. Silhouettes were constructed from the composition of several sources.

4. Discussion

Silesaurids have been considered the sister-group to Dinosauria by several authors [ 1 , 8 , 29 , 30 ]. However, an alternative hypothesis [ 31 ] considered silesaurids as ornithischian dinosaurs. Subsequently, more comprehensive studies [ 4 , 7 , 10 ] have reinforced this hypothesis. In this scenario, silesaurids are recovered within Ornithischia as the sister-group to typical ornithischians. In addition, Pisanosaurus mertii , which is historically recognized as the basalmost ornithischian, was suggested to be a silesaurid by some authors [ 15 , 32 ]. However, this new hypothesis regarding the affinities of Pisanosaurus mertii has not been recovered by subsequent studies [ 7 ] that adopt the dataset of [ 4 ], which favours the scenario where silesaurids are ornithischians. Therefore, the present results provide an alternative scenario for the Triassic radiation of ornithischians dinosaurs. Here, traditional silesaurids represent a paraphyletic array of low-diversity clades that constitute stem groups leading to core ornithischians. Based on previous definitions of Silesauridae as ‘all archosaurs closer to Silesaurus opolensis than to Heterodontosaurus tucki and Marasuchus lilloensis [ 33 ]' or ‘the most inclusive clade containing Silesaurus opolensis but not Passer domesticus , Triceratops horridus and Alligator mississippiensis ' [ 1 ], in our hypothesis, only Silesaurus opolensis and Ignotosaurus fragilis are strictly members of the clade. This is particularly interesting because it fills most of the ornithischian ghost lineages that emerge from the Triassic. Indeed, the fossil record of ornithischians from Triassic beds is completely scarce, with no unequivocal specimens known so far [ 11 ]. On the contrary, sauropodomorphs and theropods are well known, especially from the Norian onwards [ 2 , 4 , 12 , 25 , 33 ]. Following our hypothesis, the lineage that encompasses the Jurassic ornithischians evolved from ‘silesaurids' during the Middle to early Late Triassic, while typical ‘silesaurids' shared the land ecosystems with their relatives until the Late Triassic, when the group completely vanished.

The present alternative hypothesis explains the peculiar mosaic anatomy of Pisanosaurus mertii , which combines traits present in traditional silesaurids (e.g. possible ankylothecodont dentition, elongated popliteal fossa of the femur) and ornithischians (e.g. dorsally expanded coronoid process of the dentary). The taxon lies along a branch that connects the traditional silesaurids to core Ornithischia. The paraphyletic array indicates a gradual acquisition of traits in the branch that leads to core Ornithischia (see electronic supplementary material for inner character distribution), and therefore, Pisanosaurus mertii comprises a key-taxon in this scenario. New specimens will certainly help in our understanding of the initial evolution of the group. On the other hand, a monophyletic Silesauridae (constrained analysis) is five steps longer, representing a less parsimonious alternative. The same is true for a monophyletic Ornithoscelida, which is 25 steps longer. Nevertheless, the branch support and bootstrap values of the present topology are generally low (see electronic supplementary material, figure S2). However, it is not surprising. Low values occur in other topologies (traditional Ornithischia/Saurischia split and Ornithoscelida hypothesis) obtained by distinct datasets [ 29 ]. This condition is tentatively explained by high rates of homoplasy, as the earliest members of the major subgroups were very similar in body size and morphology [ 29 ].

The dinosaurian affinities of Lewisuchus admixtus is another interesting result. This taxon shares with sampled dinosaurs a mediolaterally compressed basipterygoid process of the parabasisphenoid, post glenoid process of the coracoid extending caudal to glenoid, pubis length more than 70% of femoral length, a sulcus on the dorsolateral surface of the ischium, angled ‘greater trochanter' of the femur, a transverse groove on the proximal surface of the femur and a caudolateral flange on the distal portion of the tibia. In addition, the position of Lewisuchus admixtus as the basalmost member of Ornithischia sheds lights on the ancestral anatomy and diet of the clade. The clade is widely known for their highly specialized herbivorous diet [ 34 , 35 ]. All the previous hypotheses favour omnivory/herbivory as the feeding strategy of the earliest members of Ornithischia [ 1 , 4 , 8 , 17 ]. The only exception is [ 32 ], which recovered the faunivorous Daemonosaurus chauliodus as the basalmost ornithischian. Even in the studies that support the hypothesis of silesaurids being ornithischians, the omnivorous/herbivorous diet was preferred, as these studies recover Asilisaurus kongwe as the basalmost member [ 4 , 7 , 12 ]. For some authors [ 10 ], the presence of teeth with sub-triangular crowns and a constricted root and dentaries with a beak-like anterior tip suggest omnivorous/herbivorous diet for Asilisaurus kongwe . On the other hand, the recurved tooth crowns with finely serrated margins indicate a faunivorous feeding behaviour for Lewisuchus admixtus [ 4 , 5 ]. Therefore, the present topology implies the acquisition of an omnivorous/herbivorous diet as a secondary step instead of an ancestral condition for ornithischians ( figure 2 ), similar to Sauropodomorpha [ 4 , 12 ]. The paraphyletic array reveals a gradual acquisition of dental traits related to an omnivorous/herbivorous diet across the ‘silesaurids' toward core ornithischians ( figure 1 ). For instance, sulcimentisaurs present large serrations of middle maxillary/dentary teeth forming oblique angles with the margin of the tooth, a condition shared with omnivorous/herbivorous sauropodomorphs. However, the current scenario favours the independent acquisition of an herbivorous diet for sauropodomorphs and ornithischians during the Triassic, whereas the traditional hypotheses suggested the independent acquisition for sauropodomorphs, ornithischians and silesaurids [ 1 , 29 ]. The same is true for the scenario that considers Daemonosaurus chauliodus to be the earliest ornithischian, with silesaurids representing non-dinosaur dinosauriforms [ 32 ]. Therefore, according to the present hypothesis, ornithischians were the first group of dinosaurs to adopt an omnivorous/herbivorous diet, whereas during the Late Triassic, the group shared the land ecosystems with omnivorous/herbivorous sauropodomorphs. Finally, during the Jurassic, ornithischians evolved new anatomical structures that improved their feeding strategies, while sauropodomorphs became larger and typical ‘silesaurids' went extinct.

Reduced strict consensus tree from the first phylogenetic analysis depicting feeding habits inference from the ancestral state reconstruction analysis. Silhouettes were constructed from the composition of several sources.

Supplementary Material

Acknowledgements.

The Handling Editor of Biology Letters and anonymous reviewers provided valuable comments that greatly improved this manuscript. We thank the Willi Henning Society, for the gratuity of TNT software.

Data accessibility

Data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.xgxd254dm [ 36 ].

Authors' contributions

R.T.M. and M.S.G. constructed the dataset, carried out the analyses, discussed the results and wrote the paper. Both authors approved the final version of the paper and agreed to be accountable for all aspects of the work.

Competing interests

We declare we have no competing interests.

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Title: impact of the brink axel hypothesis on unique first forbidden \b{eta} transitions for r process nuclei.

Abstract: Key nuclear inputs for the astrophysical r process simulations are the weak interaction rates. Consequently, the accuracy of these inputs directly affects the reliability of nucleosynthesis modeling. Majority of the stellar rates, used in simulation studies, are calculated invoking the Brink Axel (BA) hypothesis. The BA hypothesis assumes that the strength functions of all parent excited states are the same as for the ground state, only shifted in energies. However, BA hypothesis has to be tested against microscopically calculated state by state rates. In this project we study the impact of the BA hypothesis on calculated stellar \b{eta} decay and electron capture rates. Our investigation include both Unique First Forbidden (U1F) and allowed transitions for 106 neutron rich trans iron nuclei ([27, 77] less than equal to [Z, A] less than equal to [82, 208]). The calculations were performed using the deformed proton-neutron quasiparticle randomphase approximation (pn QRPA) model with a simple plus quadrupole separable and schematic interaction. Waiting-point and several key r process nuclei lie within the considered mass region of the nuclear chart.

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