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How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

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 .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, 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 types of variables .

An independent variable is something the researcher changes or controls.
A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias will affect your results.

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 ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize 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.

4. Refine your hypothesis

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

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

5. 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.

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 .

H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

Sampling methods
Simple random sampling
Stratified sampling
Cluster sampling
Likert scales
Reproducibility

Statistics

Null hypothesis
Statistical power
Probability distribution
Effect size
Poisson distribution

Research bias

Optimism bias
Cognitive bias
Implicit bias
Hawthorne effect
Anchoring bias
Explicit bias

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

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

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.

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5.2 - writing hypotheses.

The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis ($H_0$) and an alternative hypothesis ($H_a$).

When writing hypotheses there are three things that we need to know: (1) the parameter that we are testing (2) the direction of the test (non-directional, right-tailed or left-tailed), and (3) the value of the hypothesized parameter.

At this point we can write hypotheses for a single mean ($\mu$), paired means($\mu_d$), a single proportion ($p$), the difference between two independent means ($\mu_1-\mu_2$), the difference between two proportions ($p_1-p_2$), a simple linear regression slope ($\beta$), and a correlation ($\rho$).
The research question will give us the information necessary to determine if the test is two-tailed (e.g., "different from," "not equal to"), right-tailed (e.g., "greater than," "more than"), or left-tailed (e.g., "less than," "fewer than").
The research question will also give us the hypothesized parameter value. This is the number that goes in the hypothesis statements (i.e., $\mu_0$ and $p_0$). For the difference between two groups, regression, and correlation, this value is typically 0.

Hypotheses are always written in terms of population parameters (e.g., $p$ and $\mu$). The tables below display all of the possible hypotheses for the parameters that we have learned thus far. Note that the null hypothesis always includes the equality (i.e., =).

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Knowledge Base
Methodology
How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

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

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

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 .

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.

Cite this Scribbr article

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McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 15 April 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

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

Introduction

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

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

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

What is the simple definition of a hypothesis?

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

What is the hypothesis for in research?

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

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

What is an example of a hypothesis?

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

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

What makes a good hypothesis?

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

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

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

Null hypothesis

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

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

Alternative hypothesis

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

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

Simple hypothesis

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

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

Complex hypothesis

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

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

Directional hypothesis

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

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

Statistical hypothesis

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

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

Empirical hypothesis

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

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

Causal hypothesis

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

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

Associative hypothesis

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

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

Relational hypothesis

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

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

Logical hypothesis

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

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

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

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

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

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

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

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

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

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

Literature review

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

Research methods

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

Preliminary research

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

Data analysis

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

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Learn How To Write A Hypothesis For Your Next Research Project!

Undoubtedly, research plays a crucial role in substantiating or refuting our assumptions. These assumptions act as potential answers to our questions. Such assumptions, also known as hypotheses, are considered key aspects of research. In this blog, we delve into the significance of hypotheses. And provide insights on how to write them effectively. So, let’s dive in and explore the art of writing hypotheses together.

Table of Contents

What is a Hypothesis?

A hypothesis is a crucial starting point in scientific research. It is an educated guess about the relationship between two or more variables. In other words, a hypothesis acts as a foundation for a researcher to build their study.

Here are some examples of well-crafted hypotheses:

Increased exposure to natural sunlight improves sleep quality in adults.

A positive relationship between natural sunlight exposure and sleep quality in adult individuals.

Playing puzzle games on a regular basis enhances problem-solving abilities in children.

Engaging in frequent puzzle gameplay leads to improved problem-solving skills in children.

Students and improved learning hecks.

S tudents using online paper writing service platforms (as a learning tool for receiving personalized feedback and guidance) will demonstrate improved writing skills. (compared to those who do not utilize such platforms).

The use of APA format in research papers.

Using the APA format helps students stay organized when writing research papers. Organized students can focus better on their topics and, as a result, produce better quality work.

The Building Blocks of a Hypothesis

To better understand the concept of a hypothesis, let’s break it down into its basic components:

Variables . A hypothesis involves at least two variables. An independent variable and a dependent variable. The independent variable is the one being changed or manipulated, while the dependent variable is the one being measured or observed.
Relationship : A hypothesis proposes a relationship or connection between the variables. This could be a cause-and-effect relationship or a correlation between them.
Testability : A hypothesis should be testable and falsifiable, meaning it can be proven right or wrong through experimentation or observation.

Types of Hypotheses

When learning how to write a hypothesis, it’s essential to understand its main types. These include; alternative hypotheses and null hypotheses. In the following section, we explore both types of hypotheses with examples.

Alternative Hypothesis (H1)

This kind of hypothesis suggests a relationship or effect between the variables. It is the main focus of the study. The researcher wants to either prove or disprove it. Many research divides this hypothesis into two subsections:

Directional

This type of H1 predicts a specific outcome. Many researchers use this hypothesis to explore the relationship between variables rather than the groups.

Non-directional

You can take a guess from the name. This type of H1 does not provide a specific prediction for the research outcome.

Here are some examples for your better understanding of how to write a hypothesis.

Consuming caffeine improves cognitive performance. (This hypothesis predicts that there is a positive relationship between caffeine consumption and cognitive performance.)
Aerobic exercise leads to reduced blood pressure. (This hypothesis suggests that engaging in aerobic exercise results in lower blood pressure readings.)
Exposure to nature reduces stress levels among employees. (Here, the hypothesis proposes that employees exposed to natural environments will experience decreased stress levels.)
Listening to classical music while studying increases memory retention. (This hypothesis speculates that studying with classical music playing in the background boosts students’ ability to retain information.)
Early literacy intervention improves reading skills in children. (This hypothesis claims that providing early literacy assistance to children results in enhanced reading abilities.)
Time management in nursing students. ( Students who use a nursing research paper writing service have more time to focus on their studies and can achieve better grades in other subjects. )

Null Hypothesis (H0)

A null hypothesis assumes no relationship or effect between the variables. If the alternative hypothesis is proven to be false, the null hypothesis is considered to be true. Usually a null hypothesis shows no direct correlation between the defined variables.

Here are some of the examples

The consumption of herbal tea has no effect on sleep quality. (This hypothesis assumes that herbal tea consumption does not impact the quality of sleep.)
The number of hours spent playing video games is unrelated to academic performance. (Here, the null hypothesis suggests that no relationship exists between video gameplay duration and academic achievement.)
Implementing flexible work schedules has no influence on employee job satisfaction. (This hypothesis contends that providing flexible schedules does not affect how satisfied employees are with their jobs.)
Writing ability of a 7th grader is not affected by reading editorial example. ( There is no relationship between reading an editorial example and improving a 7th grader’s writing abilities.)
The type of lighting in a room does not affect people’s mood. (In this null hypothesis, there is no connection between the kind of lighting in a room and the mood of those present.)
The use of social media during break time does not impact productivity at work. (This hypothesis proposes that social media usage during breaks has no effect on work productivity.)

As you learn how to write a hypothesis, remember that aiming for clarity, testability, and relevance to your research question is vital. By mastering this skill, you’re well on your way to conducting impactful scientific research. Good luck!

Importance of a Hypothesis in Research

A well-structured hypothesis is a vital part of any research project for several reasons:

It provides clear direction for the study by setting its focus and purpose.
It outlines expectations of the research, making it easier to measure results.
It helps identify any potential limitations in the study, allowing researchers to refine their approach.

In conclusion, a hypothesis plays a fundamental role in the research process. By understanding its concept and constructing a well-thought-out hypothesis, researchers lay the groundwork for a successful, scientifically sound investigation.

How to Write a Hypothesis?

Here are five steps that you can follow to write an effective hypothesis.

Step 1: Identify Your Research Question

The first step in learning how to compose a hypothesis is to clearly define your research question. This question is the central focus of your study and will help you determine the direction of your hypothesis.

Step 2: Determine the Variables

When exploring how to write a hypothesis, it’s crucial to identify the variables involved in your study. You’ll need at least two variables:

Independent variable : The factor you manipulate or change in your experiment.
Dependent variable : The outcome or result you observe or measure, which is influenced by the independent variable.

Step 3: Build the Hypothetical Relationship

In understanding how to compose a hypothesis, constructing the relationship between the variables is key. Based on your research question and variables, predict the expected outcome or connection. This prediction should be specific, testable, and, if possible, expressed in the “If…then” format.

Step 4: Write the Null Hypothesis

When mastering how to write a hypothesis, it’s important to create a null hypothesis as well. The null hypothesis assumes no relationship or effect between the variables, acting as a counterpoint to your primary hypothesis.

Step 5: Review Your Hypothesis

Finally, when learning how to compose a hypothesis, it’s essential to review your hypothesis for clarity, testability, and relevance to your research question. Make any necessary adjustments to ensure it provides a solid basis for your study.

In conclusion, understanding how to write a hypothesis is crucial for conducting successful scientific research. By focusing on your research question and carefully building relationships between variables, you will lay a strong foundation for advancing research and knowledge in your field.

Hypothesis vs. Prediction: What’s the Difference?

Understanding the differences between a hypothesis and a prediction is crucial in scientific research. Often, these terms are used interchangeably, but they have distinct meanings and functions. This segment aims to clarify these differences and explain how to compose a hypothesis correctly, helping you improve the quality of your research projects.

Hypothesis: The Foundation of Your Research

A hypothesis is an educated guess about the relationship between two or more variables. It provides the basis for your research question and is a starting point for an experiment or observational study.

The critical elements for a hypothesis include:

Specificity: A clear and concise statement that describes the relationship between variables.
Testability: The ability to test the hypothesis through experimentation or observation.

To learn how to write a hypothesis, it’s essential to identify your research question first and then predict the relationship between the variables.

Prediction: The Expected Outcome

A prediction is a statement about a specific outcome you expect to see in your experiment or observational study. It’s derived from the hypothesis and provides a measurable way to test the relationship between variables.

Here’s an example of how to write a hypothesis and a related prediction:

Hypothesis: Consuming a high-sugar diet leads to weight gain.
Prediction: People who consume a high-sugar diet for six weeks will gain more weight than those who maintain a low-sugar diet during the same period.

Key Differences Between a Hypothesis and a Prediction

While a hypothesis and prediction are both essential components of scientific research, there are some key differences to keep in mind:

A hypothesis is an educated guess that suggests a relationship between variables, while a prediction is a specific and measurable outcome based on that hypothesis.
A hypothesis can give rise to multiple experiment or observational study predictions.

To conclude, understanding the differences between a hypothesis and a prediction, and learning how to write a hypothesis, are essential steps to form a robust foundation for your research. By creating clear, testable hypotheses along with specific, measurable predictions, you lay the groundwork for scientifically sound investigations.

Here’s a wrap-up for this guide on how to write a hypothesis. We’re confident this article was helpful for many of you. We understand that many students struggle with writing their school research . However, we hope to continue assisting you through our blog tutorial on writing different aspects of academic assignments.

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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.

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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.

The Craft of Writing a Strong Hypothesis

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1. Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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What is and How to Write a Good Hypothesis in Research?

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Table of Contents

One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

Predicts the relationship and outcome
Simple and concise – avoid wordiness
Clear with no ambiguity or assumptions about the readers’ knowledge
Observable and testable results
Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

Language Editing Plus

Elsevier’s Language Editing Plus service can help ensure that your research hypothesis is well-designed, and articulates your research and conclusions. Our most comprehensive editing package, you can count on a thorough language review by native-English speakers who are PhDs or PhD candidates. We’ll check for effective logic and flow of your manuscript, as well as document formatting for your chosen journal, reference checks, and much more.

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How to Write a Great Hypothesis

Hypothesis 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, operational definitions, types of hypotheses, hypotheses examples.

Collecting Data

Frequently Asked Questions

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.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "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."

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. It is only at this point that researchers 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 a number of 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 wisdom 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.

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.

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.

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

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

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 that there is a relationship between one independent variable and one dependent variable.
Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
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 sample of the population 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."
Complex hypothesis: "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."

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:

"Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
"There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

"Children who receive a new reading intervention will perform better than students who did not receive the intervention."
"Adults will perform better on a memory task than children."

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 it would be impossible or difficult to conduct an experiment . 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 then be used to look at how the variables are related. This type of 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.

A Word From Verywell

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.

Some examples of how to write a hypothesis include:

"Staying up late will lead to worse test performance the next day."
"People who consume one apple each day will visit the doctor fewer times each year."
"Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

The research question
The independent variable (IV)
The dependent variable (DV)
The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

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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Education and Communications
Science Writing

How to Write a Hypothesis

Last Updated: May 2, 2023 Fact Checked

This article was co-authored by Bess Ruff, MA . Bess Ruff is a Geography PhD student at Florida State University. She received her MA in Environmental Science and Management from the University of California, Santa Barbara in 2016. She has conducted survey work for marine spatial planning projects in the Caribbean and provided research support as a graduate fellow for the Sustainable Fisheries Group. There are 9 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 1,032,354 times.

A hypothesis is a description of a pattern in nature or an explanation about some real-world phenomenon that can be tested through observation and experimentation. The most common way a hypothesis is used in scientific research is as a tentative, testable, and falsifiable statement that explains some observed phenomenon in nature. [1] X Research source Many academic fields, from the physical sciences to the life sciences to the social sciences, use hypothesis testing as a means of testing ideas to learn about the world and advance scientific knowledge. Whether you are a beginning scholar or a beginning student taking a class in a science subject, understanding what hypotheses are and being able to generate hypotheses and predictions yourself is very important. These instructions will help get you started.

Preparing to Write a Hypothesis

If you are writing a hypothesis for a school assignment, this step may be taken care of for you.

Focus on academic and scholarly writing. You need to be certain that your information is unbiased, accurate, and comprehensive. Scholarly search databases such as Google Scholar and Web of Science can help you find relevant articles from reputable sources.
You can find information in textbooks, at a library, and online. If you are in school, you can also ask for help from teachers, librarians, and your peers.

For example, if you are interested in the effects of caffeine on the human body, but notice that nobody seems to have explored whether caffeine affects males differently than it does females, this could be something to formulate a hypothesis about. Or, if you are interested in organic farming, you might notice that no one has tested whether organic fertilizer results in different growth rates for plants than non-organic fertilizer.
You can sometimes find holes in the existing literature by looking for statements like “it is unknown” in scientific papers or places where information is clearly missing. You might also find a claim in the literature that seems far-fetched, unlikely, or too good to be true, like that caffeine improves math skills. If the claim is testable, you could provide a great service to scientific knowledge by doing your own investigation. If you confirm the claim, the claim becomes even more credible. If you do not find support for the claim, you are helping with the necessary self-correcting aspect of science.
Examining these types of questions provides an excellent way for you to set yourself apart by filling in important gaps in a field of study.

Following the examples above, you might ask: "How does caffeine affect females as compared to males?" or "How does organic fertilizer affect plant growth compared to non-organic fertilizer?" The rest of your research will be aimed at answering these questions.

Step 5 Look for clues as to what the answer might be.

Following the examples above, if you discover in the literature that there is a pattern that some other types of stimulants seem to affect females more than males, this could be a clue that the same pattern might be true for caffeine. Similarly, if you observe the pattern that organic fertilizer seems to be associated with smaller plants overall, you might explain this pattern with the hypothesis that plants exposed to organic fertilizer grow more slowly than plants exposed to non-organic fertilizer.

Formulating Your Hypothesis

You can think of the independent variable as the one that is causing some kind of difference or effect to occur. In the examples, the independent variable would be biological sex, i.e. whether a person is male or female, and fertilizer type, i.e. whether the fertilizer is organic or non-organically-based.
The dependent variable is what is affected by (i.e. "depends" on) the independent variable. In the examples above, the dependent variable would be the measured impact of caffeine or fertilizer.
Your hypothesis should only suggest one relationship. Most importantly, it should only have one independent variable. If you have more than one, you won't be able to determine which one is actually the source of any effects you might observe.

Don't worry too much at this point about being precise or detailed.
In the examples above, one hypothesis would make a statement about whether a person's biological sex might impact the way the person is affected by caffeine; for example, at this point, your hypothesis might simply be: "a person's biological sex is related to how caffeine affects his or her heart rate." The other hypothesis would make a general statement about plant growth and fertilizer; for example your simple explanatory hypothesis might be "plants given different types of fertilizer are different sizes because they grow at different rates."

Using our example, our non-directional hypotheses would be "there is a relationship between a person's biological sex and how much caffeine increases the person's heart rate," and "there is a relationship between fertilizer type and the speed at which plants grow."
Directional predictions using the same example hypotheses above would be : "Females will experience a greater increase in heart rate after consuming caffeine than will males," and "plants fertilized with non-organic fertilizer will grow faster than those fertilized with organic fertilizer." Indeed, these predictions and the hypotheses that allow for them are very different kinds of statements. More on this distinction below.
If the literature provides any basis for making a directional prediction, it is better to do so, because it provides more information. Especially in the physical sciences, non-directional predictions are often seen as inadequate.

Where necessary, specify the population (i.e. the people or things) about which you hope to uncover new knowledge. For example, if you were only interested the effects of caffeine on elderly people, your prediction might read: "Females over the age of 65 will experience a greater increase in heart rate than will males of the same age." If you were interested only in how fertilizer affects tomato plants, your prediction might read: "Tomato plants treated with non-organic fertilizer will grow faster in the first three months than will tomato plants treated with organic fertilizer."

For example, you would not want to make the hypothesis: "red is the prettiest color." This statement is an opinion and it cannot be tested with an experiment. However, proposing the generalizing hypothesis that red is the most popular color is testable with a simple random survey. If you do indeed confirm that red is the most popular color, your next step may be to ask: Why is red the most popular color? The answer you propose is your explanatory hypothesis .

An easy way to get to the hypothesis for this method and prediction is to ask yourself why you think heart rates will increase if children are given caffeine. Your explanatory hypothesis in this case may be that caffeine is a stimulant. At this point, some scientists write a research hypothesis , a statement that includes the hypothesis, the experiment, and the prediction all in one statement.
For example, If caffeine is a stimulant, and some children are given a drink with caffeine while others are given a drink without caffeine, then the heart rates of those children given a caffeinated drink will increase more than the heart rate of children given a non-caffeinated drink.

Using the above example, if you were to test the effects of caffeine on the heart rates of children, evidence that your hypothesis is not true, sometimes called the null hypothesis , could occur if the heart rates of both the children given the caffeinated drink and the children given the non-caffeinated drink (called the placebo control) did not change, or lowered or raised with the same magnitude, if there was no difference between the two groups of children.
It is important to note here that the null hypothesis actually becomes much more useful when researchers test the significance of their results with statistics. When statistics are used on the results of an experiment, a researcher is testing the idea of the null statistical hypothesis. For example, that there is no relationship between two variables or that there is no difference between two groups. [8] X Research source

Hypothesis Examples

Community Q&A

Remember that science is not necessarily a linear process and can be approached in various ways. [10] X Research source Thanks Helpful 0 Not Helpful 0
When examining the literature, look for research that is similar to what you want to do, and try to build on the findings of other researchers. But also look for claims that you think are suspicious, and test them yourself. Thanks Helpful 0 Not Helpful 0
Be specific in your hypotheses, but not so specific that your hypothesis can't be applied to anything outside your specific experiment. You definitely want to be clear about the population about which you are interested in drawing conclusions, but nobody (except your roommates) will be interested in reading a paper with the prediction: "my three roommates will each be able to do a different amount of pushups." Thanks Helpful 0 Not Helpful 0

↑ https://undsci.berkeley.edu/for-educators/prepare-and-plan/correcting-misconceptions/#a4
↑ https://owl.purdue.edu/owl/general_writing/common_writing_assignments/research_papers/choosing_a_topic.html
↑ https://owl.purdue.edu/owl/subject_specific_writing/writing_in_the_social_sciences/writing_in_psychology_experimental_report_writing/experimental_reports_1.html
↑ https://www.grammarly.com/blog/how-to-write-a-hypothesis/
↑ https://grammar.yourdictionary.com/for-students-and-parents/how-create-hypothesis.html
↑ https://flexbooks.ck12.org/cbook/ck-12-middle-school-physical-science-flexbook-2.0/section/1.19/primary/lesson/hypothesis-ms-ps/
↑ https://iastate.pressbooks.pub/preparingtopublish/chapter/goal-1-contextualize-the-studys-methods/
↑ http://mathworld.wolfram.com/NullHypothesis.html
↑ http://undsci.berkeley.edu/article/scienceflowchart

About This Article

Before writing a hypothesis, think of what questions are still unanswered about a specific subject and make an educated guess about what the answer could be. Then, determine the variables in your question and write a simple statement about how they might be related. Try to focus on specific predictions and variables, such as age or segment of the population, to make your hypothesis easier to test. For tips on how to test your hypothesis, read on! Did this summary help you? Yes No

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Sat / act prep online guides and tips, what is a hypothesis and how do i write one.

General Education

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

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

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

So let’s get started!

What Is a Hypothesis?

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

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

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

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

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

Independent and Dependent Variables

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

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

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

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

Elements of a Good Hypothesis

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

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

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

Writing Your Hypothesis

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

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

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

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

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

The Two Types of Hypotheses

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

#1: If-Then Hypotheses

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

If ____ happens, then ____ will happen.

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

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

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

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

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

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

#2: Null Hypotheses

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

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

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

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

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

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

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

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

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

4 Tips to Write the Best Hypothesis

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

#1: Plausibility

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

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

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

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

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

#2: Defined Concepts

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

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

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

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

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

Good hypotheses ensure that you can observe the results.

#3: Observability

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

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

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

#4: Generalizability

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

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

Hypothesis Testing Examples

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

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

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

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

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

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

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

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

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

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

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

Here’s what your hypotheses might look like:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Key Takeaways: Hypothesis Writing

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

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

What’s Next?

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

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

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

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

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HOW TO WRITE A HYPOTHESIS

Writing a hypothesis

Frequently, when we hear the word ‘hypothesis’, we immediately think of an investigation in the form of a science experiment. This is not surprising, as science is the subject area where we are usually first introduced to the term.

However, the term hypothesis also applies to investigations and research in many diverse areas and branches of learning, leaving us wondering how to write a hypothesis in statistics and how to write a hypothesis in sociology alongside how to write a hypothesis in a lab report.

We can find hypotheses at work in areas as wide-ranging as history, psychology, technology, engineering, literature, design, and economics. With such a vast array of uses, hypothesis writing is an essential skill for our students to develop.

What Is a Hypothesis?

how to write a hypothesis | Hypothesis definition | HOW TO WRITE A HYPOTHESIS | literacyideas.com

A hypothesis is a proposed or predicted answer to a question. The purpose of writing a hypothesis is to follow it up by testing that answer. This test can take the form of an investigation, experiment, or writing a research paper that will ideally prove or disprove the hypothesis’s prediction.

Despite this element of the unknown, a hypothesis is not the same thing as a guess. Though the hypothesis writer typically has some uncertainty, the creation of the hypothesis is generally based on some background knowledge and research of the topic. The writer believes in the likelihood of a specific outcome, but further investigation will be required to validate or falsify the claim made in their hypothesis.

In this regard, a hypothesis is more along the lines of an ‘educated guess’ that has been based on observation and/or background knowledge.

A hypothesis should:

Make a prediction
Provide reasons for that prediction
Specifies a relationship between two or more variables
Be testable
Be falsifiable
Be expressed simply and concisely
Serves as the starting point for an investigation, an experiment, or another form of testing

A COMPLETE TEACHING UNIT ON WRITING PROCEDURAL TEXTS

how to write a hypothesis | procedural text writing unit 1 | HOW TO WRITE A HYPOTHESIS | literacyideas.com

This HUGE BUNDLE offers 97 PAGES of hands-on, printable, and digital media resources. Your students will be WRITING procedures with STRUCTURE, INSIGHT AND KNOWLEDGE like never before.

Hypothesis Examples for Students and Teachers

If students listen to classical music while studying, they will retain more information.

Mold growth is affected by the level of moisture in the air.

Students who sleep for longer at night retain more information at school.

Employees who work more than 40 hours per week show higher instances of clinical depression.

Time spent on social media is negatively correlated to the length of the average attention span.

People who spend time exercising regularly are less likely to develop a cardiovascular illness.

If people are shorter, then they are more likely to live longer.

What are Variables in a Hypothesis?

Variables are an essential aspect of any hypothesis. But what exactly do we mean by this term?

Variables are changeable factors or characteristics that may affect the outcome of an investigation. Things like age, weight, the height of participants, length of time, the difficulty of reading material, etc., could all be considered variables.

Usually, an investigation or experiment will focus on how different variables affect each other. So, it is vital to define the variables clearly if you are to measure the effect they have on each other accurately.

There are three main types of variables to consider in a hypothesis. These are:

Independent Variables
Dependent Variables

The Independent Variable

The independent variable is unaffected by any of the other variables in the hypothesis. We can think of the independent variable as the assumed cause .

The Dependent Variable

The dependent variable is affected by the other variables in the hypothesis. It is what is being tested or measured. We can think of the dependent variable as the assumed effect .

For example, let’s investigate the correlation between test scores across different age groups. The age groups will be the independent variable, and the test scores will be the dependent variable .

Now that we know what variables are let’s look at how they work in the various types of hypotheses.

Types of Hypotheses

There are many different types of hypotheses, and it is helpful to know the most common of these if the student selects the most suitable tool for their specific job.

The most frequently used types of hypotheses are:

The Simple Hypothesis

The complex hypothesis, the empirical hypothesis, the null hypothesis, the directional hypothesis, the non-directional hypothesis.

This straightforward hypothesis type predicts the relationship between an independent and dependent variable.

Example: Eating too much sugar causes weight gain.

This type of hypothesis is based on the relationship between multiple independent and/or dependent variables.

Example: Overeating sugar causes weight gain and poor cardiovascular health.

Also called a working hypothesis, an empirical hypothesis is tested through observation and experimentation. An empirical hypothesis is produced through investigation and trial and error. As a result, the empirical hypothesis may change its independent variables in the process.

Example: Exposure to sunlight helps lettuces grow faster.

This hypothesis states that there is no significant or meaningful relationship between specific variables.

Example: Exposure to sunlight does not affect the rate of a plant’s growth.

This type of hypothesis predicts the direction of an effect between variables, i.e., positive or negative.

Example: A high-quality education will result in a greater number of career opportunities.

Similar to the directional hypothesis, this type of hypothesis predicts the nature of the effect but not the direction that effect will go in.

Example: A high-quality education will affect the number of available career opportunities.

How to Write a Hypothesis : A STEP-BY-STEP GUIDE

Ask a Question

The starting point for any hypothesis is asking a question. This is often called the research question . The research question is the student’s jumping-off point to developing their hypothesis. This question should be specific and answerable. The hypothesis will be the point where the research question is transformed into a declarative statement.

Ideally, the questions the students develop should be relational, i.e., they should look at how two or more variables relate to each other as described above. For example, what effect does sunlight have on the growth rate of lettuce?

Research the Question

The research is an essential part of the process of developing a hypothesis. Students will need to examine the ideas and studies that are out there on the topic already. By examining the literature already out there on their topic, they can begin to refine their questions on the subject and begin to form predictions based on their studies.

Remember, a hypothesis can be defined as an ‘educated’ guess. This is the part of the process where the student educates themself on the subject before making their ‘guess.’

Define Your Variables

By now, your students should be ready to form their preliminary hypotheses. To do this, they should first focus on defining their independent and dependent variables. Now may be an excellent opportunity to remind students that the independent variables are the only variables that they have complete control over, while dependent variables are what is tested or measured.

Develop Your Preliminary Hypotheses

With variables defined, students can now work on a draft of their hypothesis. To do this, they can begin by examining their variables and the available data and then making a statement about the relationship between these variables. Students must brainstorm and reflect on what they expect to happen in their investigation before making a prediction upon which to base their hypothesis. It’s worth noting, too, that hypotheses are typically, though not exclusively, written in the present tense.

Students revisit the different types of hypotheses described earlier in this article. Students select three types of hypotheses and frame their preliminary hypotheses according to each criteria. Which works best? Which type is the least suitable for the student’s hypothesis?

Finalize the Phrasing

By now, students will have made a decision on which type of hypothesis suits their needs best, and it will now be time to finalize the wording of their hypotheses. There are various ways that students can choose to frame their hypothesis, but below, we will examine the three most common ways.

The If/Then Phrasing

This is the most common type of hypothesis and perhaps the easiest to write for students. It follows a simple ‘ If x, then y ’ formula that makes a prediction that forms the basis of a subsequent investigation.

If I eat more calories, then I will gain weight.

Correlation Phrasing

Another way to phrase a hypothesis is to focus on the correlation between the variables. This typically takes the form of a statement that defines that relationship positively or negatively.

The more calories that are eaten beyond the daily recommended requirements, the greater the weight gain will be.

Comparison Phrasing

This form of phrasing is applicable when comparing two groups and focuses on the differences that the investigation is expected to reveal between those two groups.

Those who eat more calories will gain more weight than those who eat fewer calories.

Questions to ask during this process include:

What tense is the hypothesis written in?
Does the hypothesis contain both independent and dependent variables?
Is the hypothesis framed using the if/then, correlation, or comparison framework (or other similar suitable structure)?
Is the hypothesis worded clearly and concisely?
Does the hypothesis make a prediction?
Is the prediction specific?
Is the hypothesis testable?
Gather Data to Support/Disprove Your Hypothesis

If the purpose of a hypothesis is to provide a reason to pursue an investigation, then the student will need to gather related information together to fuel that investigation.

While, by definition, a hypothesis leans towards a specific outcome, the student shouldn’t worry if their investigations or experiments ultimately disprove their hypothesis. The hypothesis is the starting point; the destination is not preordained. This is the very essence of the scientific method. Students should trust the results of their investigation to speak for themselves. Either way, the outcome is valuable information.

TOP 10 TIPS FOR WRITING A STRONG HYPOTHESIS

Begin by asking a clear and compelling question. Your hypothesis is a response to the inquiry you are eager to explore.
Keep it simple and straightforward. Avoid using complex phrases or making multiple predictions in one hypothesis.
Use the right format. A strong hypothesis is often written in the form of an “if-then” statement.
Ensure that your hypothesis is testable. Your hypothesis should be something that can be verified through experimentation or observation.
Stay objective. Your hypothesis should be based on facts and evidence, not personal opinions or prejudices.
Examine different possibilities. Don’t limit yourself to just one hypothesis. Consider alternative explanations for your observations.
Stay open to the possibility of being wrong. Your hypothesis is just a prediction, and it may not always be correct.
Search for evidence to support your hypothesis. Investigate existing literature and gather data that supports your hypothesis.
Make sure that your hypothesis is pertinent. Your hypothesis should be relevant to the question you are trying to investigate.
Revise your hypothesis as necessary. If new evidence arises that contradicts your hypothesis, you may need to adjust it accordingly.

HYPOTHESIS TEACHING STRATEGIES AND ACTIVITIES

When teaching young scientists and writers, it’s essential to remember that the process of formulating a hypothesis is not always straightforward. It’s easy to make mistakes along the way, but with a bit of guidance, you can ensure your students avoid some of the most common pitfalls like these.

Don’t let your students be too vague. Remind them that when formulating a hypothesis, it’s essential to be specific and avoid using overly general language. Make sure their hypothesis is clear and easy to understand.
Being swayed by personal biases will impact their hypothesis negatively. It’s important to stay objective when formulating a hypothesis, so avoid letting personal biases or opinions get in the way.
Not starting with a clear question is the number one stumbling block for students, so before forming a hypothesis, you need to reinforce the need for a clear understanding of the question they’re trying to answer. Start with a question that is specific and relevant.

Hypothesis Warmup Activity: First, organize students into small working groups of four or five. Then, set each group to collect a list of hypotheses. They can find these by searching on the Internet or finding examples in textbooks . When students have gathered together a suitable list of hypotheses, have them identify the independent and dependent variables in each case. They can underline each of these in different colors.

It may be helpful for students to examine each hypothesis to identify the ‘cause’ elements and the ‘effect’ elements. When students have finished, they can present their findings to the class.

Task 1: Set your students the task of coming up with an investigation-worthy question on a topic that interests them. This activity works particularly well for groups.

Task 2: Students search for existing information and theories on their topic on the Internet or in the library. They should take notes where necessary and begin to form an assumption or prediction based on their reading and research that they can investigate further.

Task 3: When working with a talking partner, can students identify which of their partner’s independent and dependent variables? If not, then one partner will need to revisit the definitions for the two types of variables as outlined earlier.

Task 4: Organize students into smaller groups and task them with presenting their hypotheses to each other. Students can then provide feedback before the final wording of each hypothesis is finalized.

Perhaps due to their short length, learning how to create a well-written hypothesis is not typically afforded much time in the curriculum.

However, though they are brief in length, they are complex enough to warrant focused learning and practice in class, particularly given their importance across many curriculum areas.

Learning how to write a hypothesis works well as a standalone writing skill. It can also form part of a more comprehensive academic or scientific writing study that focuses on how to write a research question, develop a theory, etc.

As with any text type, practice improves performance. By following the processes outlined above, students will be well on their way to writing their own hypotheses competently in no time.

Inferential Statistics

Inferential Statistics – Definition, Types, Examples, Formulas
Observational Studies and Experiments
Sample and Population
Sampling Bias
Sampling Methods
Confounding Variables
Causal Conclusions
Independent and Paired Samples
Control and Placebo Groups
Population Distribution, Sample Distribution and Sampling Distribution
Central Limit Theorem
Point Estimates
Confidence Intervals
Introduction to Bootstrapping
Bootstrap Confidence Interval
Paired Samples
Impact of Sample Size on Confidence Intervals

Introduction to Hypothesis Testing

Writing hypotheses, hypotheses test examples.

Randomization Procedures
Type I and Type II Errors
P-value Significance Level
Issues with Multiple Testing
Confidence Intervals and Hypothesis Testing
One Sample Proportion
One Sample Mean & t Distribution
Inference for Paired Means
Inference for Two Independent Proportions
Inference for Two Independent Means
Introduction to the F Distribution
One-way ANOVA hypothesis test
Two-Way ANOVA
Chi-Square Goodness of Fit Test
Chi-Square Test of Independence

The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis (H0) and an alternative hypothesis (Ha).

How to conduct a hypothesis test

A hypothesis is a statement that proposes a relationship between variables or an explanation for a phenomenon. It is an essential part of the scientific method and is used to guide the research process. Here are the steps for writing a hypothesis:

Identify the research question: Before writing a hypothesis, you need to identify the research question you want to answer.
State the null hypothesis: The null hypothesis (H0) is the default assumption that there is no significant difference or relationship between variables. It is usually stated first and is used to compare against the alternative hypothesis (Ha).
State the alternative hypothesis: The alternative hypothesis (Ha) is the opposite of the null hypothesis and proposes a specific relationship or difference between variables.
Determine the type of hypothesis: There are two types of hypotheses: directional and nondirectional. A directional hypothesis predicts the direction of the relationship or difference between variables (e.g., “increased exercise will result in decreased body weight”). A nondirectional hypothesis does not predict the direction of the relationship or difference (e.g., “there will be a difference in body weight between the exercise group and the control group”).
Make sure your hypothesis is testable: A hypothesis must be testable and falsifiable through empirical evidence.

Refine and revise the hypothesis: After stating the hypothesis, refine and revise it based on feedback and further research.

Example of a hypothesis

Research question: Does sleep affect memory consolidation?

Null hypothesis: There is no significant difference in memory consolidation between individuals who sleep for 8 hours versus those who sleep for 4 hours.

Alternative hypothesis: Individuals who sleep for 8 hours will have better memory consolidation than those who sleep for 4 hours.

Type of hypothesis: Directional

This hypothesis could be tested through an experimental study in which participants are randomly assigned to either an 8-hour or 4-hour sleep condition and then tested on a memory task. The results could be analyzed to determine if there is a significant difference in memory consolidation between the two conditions.

So, now we know that When writing hypotheses there are three things that we need to know:

(1) the parameter that we are testing
(2) the direction of the test (non-directional, right-tailed or left-tailed), and
(3) the value of the hypothesized parameter.

Now you know that, when writing hypotheses there are three things that we need to know: (1) the parameter that we are testing (2) the direction of the test (non-directional, right-tailed or left-tailed), and (3) the value of the hypothesized parameter.

We can write hypotheses for a single mean (µ), paired means(µd), a single proportion (p), the difference between two independent means (µ1-µ2), the difference between two proportions (p1-p2), a simple linear regression slope (β), and a correlation (ρ).
The research question will give us the information necessary to determine if the test is two-tailed (e.g., “different from,” “not equal to”), right-tailed (e.g., “greater than,” “more than”), or left-tailed (e.g., “less than,” “fewer than”).
The research question will also give us the hypothesized parameter value. This is the number that goes in the hypothesis statements (i.e., µ0 and p0). For the difference between two groups, regression, and correlation, this value is typically 0.

One Group Mean

Null Hypothesis: The population mean is equal to a specific value. Alternative Hypothesis: The population mean is not equal to a specific value. Example: H0: µ = 50, Ha: µ ≠ 50
Null Hypothesis: The population mean is less than or equal to a specific value. Alternative Hypothesis: The population mean is greater than a specific value. Example: H0: µ ≤ 10, Ha: µ > 10
Null Hypothesis: The population mean is greater than or equal to a specific value. Alternative Hypothesis: The population mean is less than a specific value. Example: H0: µ ≥ 80, Ha: µ < 80

Paired Means

Null Hypothesis: The mean difference between two paired samples is equal to zero. Alternative Hypothesis: The mean difference between two paired samples is not equal to zero. Example: H0: µd = 0, Ha: µd ≠ 0
Null Hypothesis: The mean difference between two paired samples is less than or equal to zero. Alternative Hypothesis: The mean difference between two paired samples is greater than zero. Example: H0: µd ≤ 0, Ha: µd > 0
Null Hypothesis: The mean difference between two paired samples is greater than or equal to zero. Alternative Hypothesis: The mean difference between two paired samples is less than zero. Example: H0: µd ≥ 2, Ha: µd < 2

Note: In the above hypotheses, x̄ represents the sample mean, µ represents the population mean, µd represents the mean difference between two paired samples, and H0 and Ha represent the null and alternative hypotheses, respectively

One Group Proportion

Null Hypothesis: The proportion of adults who own a car is 60%. Alternative Hypothesis: The proportion of adults who own a car is not 60%. Example: H0: p = 0.60, Ha: p ≠ 0.60
Null Hypothesis: The proportion of customers who are satisfied with the service is less than or equal to 0.75. Alternative Hypothesis: The proportion of customers who are satisfied with the service is greater than 0.75. Example: H0: p ≤ 0.75, Ha: p > 0.75
Null Hypothesis: The proportion of students who pass the exam is greater than or equal to 0.85. Alternative Hypothesis: The proportion of students who pass the exam is less than 0.85. Example: H0: p ≥ 0.85, Ha: p < 0.85

Difference between Two Independent Means

Null Hypothesis (H0): There is no significant difference between the means of two independent groups. Alternative Hypothesis (Ha): There is a significant difference between the means of two independent groups (two-tailed).
Null Hypothesis (H0): The mean of the population is less than or equal to a certain value. Alternative Hypothesis (Ha): The mean of the population is greater than the certain value (right-tailed).
Null Hypothesis (H0): The mean of the population is greater than or equal to a certain value. Alternative Hypothesis (Ha): The mean of the population is less than the certain value (left-tailed).

Difference between Two Proportions

Null Hypothesis (H0): There is no significant difference between the proportions of two independent groups. Alternative Hypothesis (Ha): There is a significant difference between the proportions of two independent groups (two-tailed).
Null Hypothesis (H0): The proportion of one group is less than or equal to the proportion of another group. Alternative Hypothesis (Ha): The proportion of one group is greater than the proportion of another group (right-tailed).
Null Hypothesis (H0): The proportion of one group is greater than or equal to the proportion of another group. Alternative Hypothesis (Ha): The proportion of one group is less than the proportion of another group (left-tailed).

To test this hypothesis, statistical methods such as a two-sample z-test or chi-square test can be used to determine if the difference between the two proportions is statistically significant or if it could have occurred by chance.

Simple Linear Regression: Slope

Null Hypothesis (H0): There is no significant linear relationship between the predictor variable and the response variable. Alternative Hypothesis (Ha): There is a significant linear relationship between the predictor variable and the response variable, and the slope of the regression line is not equal to zero (two-tailed).
Null Hypothesis (H0): There is no significant linear relationship between the predictor variable and the response variable or the slope of the regression line is less than or equal to zero. Alternative Hypothesis (Ha): There is a significant positive linear relationship between the predictor variable and the response variable, and the slope of the regression line is greater than zero (right-tailed).
Null Hypothesis (H0): There is no significant linear relationship between the predictor variable and the response variable or the slope of the regression line is greater than or equal to zero. Alternative Hypothesis (Ha): There is a significant negative linear relationship between the predictor variable and the response variable, and the slope of the regression line is less than zero (left-tailed).

To test this hypothesis, statistical methods such as a t-test or F-test can be used to determine if the slope of the regression line is significantly different from zero, indicating a significant linear relationship between the predictor and response variables.

Correlation (Pearson’s r )

Null Hypothesis (H0): There is no significant linear relationship between the two variables. Alternative Hypothesis (Ha): There is a significant linear relationship between the two variables (two-tailed).
Null Hypothesis (H0): There is no significant positive linear relationship between the two variables. Alternative Hypothesis (Ha): There is a significant positive linear relationship between the two variables (right-tailed).
Null Hypothesis (H0): There is no significant negative linear relationship between the two variables. Alternative Hypothesis (Ha): There is a significant negative linear relationship between the two variables (left-tailed).

In this context, the Pearson’s correlation coefficient (r) measures the strength and direction of the linear relationship between two variables. A positive r value indicates a positive linear relationship (i.e., as one variable increases, so does the other), while a negative r value indicates a negative linear relationship (i.e., as one variable increases, the other decreases).

To test these hypotheses, statistical methods such as a t-test or z-test can be used to determine if the correlation coefficient is significantly different from zero and whether the relationship is positive or negative.

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Lab 1 Principles of Scientific Inquiry and the Scientific Method in the Anatomy and Physiology Lab

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Learning Objectives. Completion of this lab exercise ensures that you will be able to… 1. Describe the difference between a scientific observation and a non-scientific observation 2. Outline the steps necessary to perform an experiment via the scientific method 3. Write a hypothesis to explain an observation that you made about how the human body functions.

Pre-Lab Exercise:

True/False: A hypothesis is the same as a guess as to what will happen in an experiment.
All scientific experiment begins with either an or a about a phenomenon.
True/False: Being a scientist means following the scientific method of investigation to what is true and challenging your thoughts and assumptions about a topic through experimentation.
Hypothesis should never: .
One logical fallacy that you are aware of having made in the past is .

Science and the Scientific Method

Science is a process of learning and acquiring knowledge, and any person that does science is a “scientist.” This means that at any point in time we can all be scientists. The only separation between students learning about human anatomy and physiology, and those in lab coats working to discover the cure for a disease is the approach to understanding a phenomenon. The person performing as a “scientist” will work through a problem in a methodical approach that follows the logical rules of science. The most important part of the rules of science is to not have a prejudiced view of what will happen. Yes, you will have an idea based on previous experience, but you must approach every problem as a new experience. When you approach each problem that you wish to solve as a new experience then you are beginning to use the scientific approach and thus are using science. There is no set way on how to do science beyond following the general rules of science. The rules of science, and the scientific method, are intended to make the process as objective as possible, and thereby gain some level of understanding that is as close to a “true reality” as possible. One constant theme is that there is no certainty in science, only levels of probability and possibility for explaining the phenomenon being studied. Because of this, scientific understanding can always be challenged, or even changed, with new observations, or different interpretations of previous observations. New tools and techniques have resulted in new observations, sometimes forcing revision of what had been taken as fact in the past. With this ever-changing understanding and base of evidence there is only one certainty about science, which is, science does not “prove” it only allows for observations and explanations of those observations based on the assumptions that govern modern science. Underlying the world of modern science are four major assumptions that must be held as being true for observations to accurate, valid and reliable. First of these assumptions is that the world is real, and that the physical universe exists and is not just our imagination. This is regardless of whether, or not, it is something that can be sensed. Secondly, humans (or animals) can accurately perceive the real world, and that humans can express such understanding. Thirdly, processes that occur in nature can sufficiently explain the existence of the physical universe that we live in. Lastly, that the process being observed operates the same way, everywhere and at all times within the physical universe. The way that we use these assumptions to study the human body and any other natural phenomenon is through the scientific method. This method of learning follows a set of rules that leads to a process of asking and answering questions to search for cause-and-effect relationships in a phenomenon, or action, that we are examining. There are several rules of science that must be followed at all times. First, all explanations must be based on careful observations that come through testing a hypothesis to explain a phenomenon. Second, any hypothesis has a possibility and probability of being disproven (evidence from observation does not support the hypothesis). Third, conclusions cannot be based simply on one’s opinion or belief (popular or otherwise) about the phenomenon. Fourth, any observations and explanations must be based on the natural physical universe that can be sensed and perceived by everyone, in the same way at all times. Fifth, that the best hypothesis is the choice for explaining the phenomenon that has the greatest amount of factual support, is based on logical analysis and makes the least number of assumptions to “best fit” all of the facts from the observations. Lastly, science is not democratically fair! It is based on empirical evidence (observations) that stem from the logical flow of critical thought and analysis following the rules of science. It is very important to remember that the use of the scientific method is not developed to “prove” something “true”. Instead, it is intended to test how, what, when, where, and why something occurred. Because of this, we tend to think of the outcome of the use of the scientific method as leading to a contingent base of knowledge, rather than an absolute base of knowledge. That understanding a phenomenon is based on what evidence can support our understanding at any given point in time. This evidence changes over time and therefore our understanding of the phenomenon changes. How do we do this investigation? The scientific method is a logical process that can be viewed as a sequential step-wise process that follows these key steps:

Observation
Question about Observation: What? When? Where? Why? How? for the observation
Hypothesis: explanation of the observation that answers the question based on deductive reasoning and understanding of the principles of science and previous knowledge
Experimentation and Recording of Results: Reproducible step-wise sequence of events based on the phenomenon and scientific principles that tests the validity of the hypothesis where observed responses are recorded objectively
Analysis of Results: logical statistical analysis of results used to accept or refute hypothesis
Interpretation and Inference of Analysis: logical process using inductive reasoning and inferential thinking to explain how analysis of results and observations made during the experimentation fits within understanding of the phenomenon and expands our overall knowledge of the principles and laws governing the phenomenon. Used to indicate the support for the hypothesis being tested
Conclusion: terminal argument of inductive reasoning based on the principles of logical thought referred to as Occam’s Razor (the most logical explanation with the fewest number of assumptions is the most likely to be true) that terminates one cycle of the scientific method and can serve as the next hypothesis in a reproduced experiment

How to Observe as a Scientist

In order to be a good scientist and follow the scientific method of experimentation, we must be good at making observations. The means of observing is based on how we develop and execute our experiment. This is done through defining and controlling for factors that might impact the phenomenon that is being studied. When designing an experiment, it is important that we are changing very little in the overall factors that can impact the phenomenon so that we can indicate that changes to one item causes something else to vary in a predictable way that allows us to draw a conclusion. The factors that are being observed and controlled are called variables. There are two primary categories that all variables will fall into based on the amount of control that you have on the variable at any point of time of the experiment. Those that you are able to change, or manipulate, are termed independent variables . This variable can sometimes be referred to as the test condition. While those that you have no control over, unable to change, are termed dependent variables . The dependent variables are the variables that are being measured in any experiments. Along with these variables, there are other factors that have the ability to impact our observations. Those factors that we attempt to ensure don’t change or impact our experimental observations are referred to as control variables . In the studying of the human body, control variables typically fall under what is called “environmental conditions'' that is where we set up the test environment to have the participants of the study seen under the same condition (temperature, time of day, humidity, food). There are also factors that we might try to control but have no control over yet will impact the dependent variable by acting as a different test condition. The variables that act as second independent variables are called confounding variables . When we do analysis and interpretation, we will take into consideration these confounding variables, as their influence will impact the assumptions that are made during the interpretation of the results. Underlying scientific observations are four major assumptions that must be held as being true for observations to be accurate, valid and reliable. First of these assumptions is that the world is real, and that the physical universe exists and is not just our imagination. This is regardless of whether, or not, it is something that can be sensed. Secondly, humans (or animals) can accurately perceive the real world, and that humans can express such understanding. Thirdly, processes that occur in nature can sufficiently explain the existence of the physical universe that we live in. Lastly, that the process being observed operates the same way, everywhere and at all times within the physical universe. The ability to ensure that your observations are valid and that the confounding variables are minimized makes for good experimental observations. The ability to do this is through the constant recording of observations and journaling methods used to make the observations. The more thorough the notes you take during experimentation, the more controlled the experiment, the more likely your results are to be correct, and the more reproducible your experiment.

How to write a hypothesis

A hypothesis is your explanation of a phenomenon based on your understanding of the laws and principles of physiology. The statement is formed via a process known as deductive reasoning. A process that forces you to use what information you already know to explain why the observation that you are making has occurred. It is not as many of you have learned previously a guess or a prediction. It is also not the explanation of observation based on the experimental outcome, that is the conclusion. The conclusion can be seen as a possible hypothesis, but only for subsequent studies and experiments within the reproduction of the experimentation that is at the heart of scientific inquiry. The hypothesis you have to view as your best explanation for why and how something occurs.

As such, it should be formulated and written in such a way so as to be supported or refuted by the evidence collected in the experiment. It is not a guess as to what might happen (that is a prediction) or an “if… then” statement (as this cannot be supported or refuted). It is the explanation that you are going to test within the experiment. Being a good scientist means that your explanation will constantly undergo refinement and rewriting as new evidence and experimental analysis provides us with a greater understanding.

What are the rules for writing the hypothesis? When you write a hypothesis, there are a few key steps that need to be remembered for writing. First, there is never a “good” or a “bad” hypothesis, just one that is not well written. Second, there is never a “right” or a “wrong” hypothesis, just one that gets supported (shown to be possibly true) or refuted (shown to possibly be false). Third, a hypothesis is always written to be tested by evidence that can be seen and measured.

Writing and Developing Hypothesis:

In order to develop the hypothesis, you need to develop the summary report (What am I studying? Why am I studying it? What am I going to do (what are the key steps I need to remember) in the experiment? What are my test conditions and measurements?
Be sure to focus on the purpose or the question that serves as the foundation for the experiment.
The hypothesis needs to be written as a testable claim about the relationship between test condition (what you are doing) and measurements being collected (dependent variables).
Use “if…then…” thinking
Form a tentative or conclusive claim
Be written to be too general (or specific) for the stated question that is being studied
Summarize the principle being studied
Hypothesis never is written as the statement of the law or principle being studied, or a restatement of the purpose.
Wording of the hypothesis must be open to interpretation that leads to it either being supported or refuted by the results of the experiment.

Poor Example : Individuals will breathe more when exercising. (Statement that summarizes the tri-phasic response)

Good Example : Females will have larger changes in tidal volumes than males of similar level of activity during and following a period of exercise.

References:

Clark, JE. 2010. Don’t Worry, It’s Only Science . TeachersPayTeachers. http://www.teacherspayteachers.com

ACTIVITY 1: Scientific Method and Hypothesis Testing

Watch the video that your instructor shows the class
Based on what you see, make a hypothesis that would explain what you just watched.

3. Share your hypothesis with your group and rewrite based on feedback provided

4. Have your instructor check your hypothesis and within your group agree on 1 hypothesis to use for testing through experimentation.

5. Develop the basic steps for your experiment.

ACTIVITY 2: Discussion and Interpretation and Analysis of Results

Discussion:

Discussions are written as well-organized paragraphs that allow you to discuss the meaning of your results from the experiment. This is not a summary of what you did, that is the methods. The discussion involves using inductive reasoning and inferential thinking to formulate explain both what happened and why it happened. This explanation involves using the principles to explain the observations and the observations to show how the principles hold to be true. In performing your interpretation, you are linking your observed experimental results with the principles of human physiology. This means you have to go back to what we already know about how the human body functions and then apply what do the results say about this understanding. You have to do this by limiting your assumptions and following a logical process of thinking. The logical process means that if you commit a fallacy in your thinking, then the entirety of your interpretation becomes nullified. To make sure that you do not make these fallacies, be aware of these very common mistakes:

Hasty Generalization: This is a conclusion based on insufficient or biased evidence. In other words, you are rushing to a conclusion before you have all the relevant facts.
Post hoc ergo propter hoc/Because it happened last it must be the cause: This is a conclusion that assumes that if 'A' occurred after 'B' then 'B' must have caused 'A.'
Begging the Claim: The conclusion provided is proven as being validated within the claim of the conclusion.
Petitio principii /Circular Argument: This restates the argument rather than actually proving it.
Either/or: This is a conclusion that oversimplifies the argument by reducing it to only two sides or choices.
Ad populum/Bandwagon Appeal: This is an appeal that presents what most people, or a group of people thinks, in order to persuade one to think the same way.
Ignoratio elenchi/Red Herring: This diversionary tactic avoids the key issues, often by avoiding opposing arguments rather than addressing them.
Straw Man: This move oversimplifies an opponent's viewpoint and then attacks that hollow argument.
Argumentum ad ignorantiam/Appeal to ignorance: A conclusion that offers no proof of anything except that you don’t know something
False dilemma/False dichotomy: Conclusion that limits all options down to two supposed counter points and offers opinion that manipulates the argument into an either or statement
Slippery slope: Conclusion offers argument that outcome will likely lead to further outcomes that do not logically flow due to lack of evidence
Tu Quoque: conclusion does not provide argument but distracts from the argument due to a position that the opposite occurs from a hypocrisy in the opposing viewpoint
Ambiguity/Equivocation: Conclusion that confuses and misleads by stipulating that one thing is equal to something else
Non sequitur: a conclusion does not follow logically from what preceded it.
Argumentum ad verecundiam/Appeal to Authority: Conclusion that is justified because of citation of an experiment that agrees with the conclusion
Non causa pro causa/Causal Fallacy: any logical breakdown when identifying a cause based on argument revolving around unproven causal relationship

When writing your discussion, think about the following (do not answer them as individual questions, instead use them to guide your thinking about the data and the analysis): What does the results of the statistical analysis tell you about the relationship between observations? What does the correlations tell you about the relationship between the observations ? (Think about: What is the importance of understanding the correlative values between measures (think about: What does the correlations tell me? Does having a correlation between values mean anything about the change in one causing a change in the other?) ? How does the relationship determined by your analysis align with the hypothesis (is it supported or refuted)? What are limitations that could interfere with your ability to infer or induce a conclusion? ( Think about: Where are the differences in the measures? What does differences in measurements indicate? What does the similarity between measures indicate? What errors might have occurred? How might errors in the experiment or analysis impact my results? How might accuracy impact the experimental conclusions that can be drawn?)

1. Use the following set of data from an experiment that tested the hypothesis: Males will be larger and thus stronger than females.

2. Working as a group and based on what you are given, develop a discussion (based on your understanding of human anatomy and physiology) to explain the findings.

Statistics Made Easy

How to Write a Null Hypothesis (5 Examples)

A hypothesis test uses sample data to determine whether or not some claim about a population parameter is true.

Whenever we perform a hypothesis test, we always write a null hypothesis and an alternative hypothesis, which take the following forms:

H 0 (Null Hypothesis): Population parameter =, ≤, ≥ some value

H A (Alternative Hypothesis): Population parameter <, >, ≠ some value

Note that the null hypothesis always contains the equal sign .

We interpret the hypotheses as follows:

Null hypothesis: The sample data provides no evidence to support some claim being made by an individual.

Alternative hypothesis: The sample data does provide sufficient evidence to support the claim being made by an individual.

For example, suppose it’s assumed that the average height of a certain species of plant is 20 inches tall. However, one botanist claims the true average height is greater than 20 inches.

To test this claim, she may go out and collect a random sample of plants. She can then use this sample data to perform a hypothesis test using the following two hypotheses:

H 0 : μ ≤ 20 (the true mean height of plants is equal to or even less than 20 inches)

H A : μ > 20 (the true mean height of plants is greater than 20 inches)

If the sample data gathered by the botanist shows that the mean height of this species of plants is significantly greater than 20 inches, she can reject the null hypothesis and conclude that the mean height is greater than 20 inches.

Read through the following examples to gain a better understanding of how to write a null hypothesis in different situations.

Example 1: Weight of Turtles

A biologist wants to test whether or not the true mean weight of a certain species of turtles is 300 pounds. To test this, he goes out and measures the weight of a random sample of 40 turtles.

Here is how to write the null and alternative hypotheses for this scenario:

H 0 : μ = 300 (the true mean weight is equal to 300 pounds)

H A : μ ≠ 300 (the true mean weight is not equal to 300 pounds)

Example 2: Height of Males

It’s assumed that the mean height of males in a certain city is 68 inches. However, an independent researcher believes the true mean height is greater than 68 inches. To test this, he goes out and collects the height of 50 males in the city.

H 0 : μ ≤ 68 (the true mean height is equal to or even less than 68 inches)

H A : μ > 68 (the true mean height is greater than 68 inches)

Example 3: Graduation Rates

A university states that 80% of all students graduate on time. However, an independent researcher believes that less than 80% of all students graduate on time. To test this, she collects data on the proportion of students who graduated on time last year at the university.

H 0 : p ≥ 0.80 (the true proportion of students who graduate on time is 80% or higher)

H A : μ < 0.80 (the true proportion of students who graduate on time is less than 80%)

Example 4: Burger Weights

A food researcher wants to test whether or not the true mean weight of a burger at a certain restaurant is 7 ounces. To test this, he goes out and measures the weight of a random sample of 20 burgers from this restaurant.

H 0 : μ = 7 (the true mean weight is equal to 7 ounces)

H A : μ ≠ 7 (the true mean weight is not equal to 7 ounces)

Example 5: Citizen Support

A politician claims that less than 30% of citizens in a certain town support a certain law. To test this, he goes out and surveys 200 citizens on whether or not they support the law.

H 0 : p ≥ .30 (the true proportion of citizens who support the law is greater than or equal to 30%)

H A : μ < 0.30 (the true proportion of citizens who support the law is less than 30%)

Additional Resources

Introduction to Hypothesis Testing Introduction to Confidence Intervals An Explanation of P-Values and Statistical Significance

Published by Zach

If you're stranded on an island, a 'HELP' sign can actually save you — but there's an even better way to get rescued

Three men stranded on a Pacific island were rescued after creating a "HELP" sign with palm leaves.
A survival expert said lighting three fires is an effective way to send a distress signal.
She also praised the men and said the key thing is to be creative and find any way to send a signal.

Three mariners stranded on an island in the Pacific Ocean were rescued earlier this month after they made a large "HELP" signal by laying out palm leaves on the beach — a scene right out of a movie.

The men, all in their 40s, left Polowat Atoll, a tiny coral island that's part of the Federated States of Micronesia, on March 31, traveling in a small, 20-foot skiff. Nearly a week later on April 6, a relative reported them missing.

On April 9, US military forces rescued the group from Pikelot Atoll, another tiny island in Micronesia about 1,000 miles north of Papa New Guinea and around 100 nautical miles from where the men set out.

"In a remarkable testament to their will to be found, the mariners spelled out 'HELP' on the beach using palm leaves, a crucial factor in their discovery. This act of ingenuity was pivotal in guiding rescue efforts directly to their location" Lt. Chelsea Garcia, the search and rescue mission coordinator at the time the men were found, said in a statement from the Coast Guard.

A US Navy P-8 Poseidon aircraft discovered the mariners. They dropped survival packages, while the Coast Guard Cutter Oliver Henry was rerouted to Pikelot to rescue the men, whose boat had been damaged.

The universal rule of three

Cat Bigney, a survival consultant and instructor at the Boulder Outdoor Survival School, said that often the best way to get spotted by a rescue team is to start a fire, as a big smoke stack can be spotted from miles away.

And not just one fire, but ideally three. Three of anything is considered the universal signal of distress : three fires, three blows in a whistle, three gunshots. Think of the original SOS call, the morse code distress signal which consists of three dots, three dashes, and three dots.

Bigney said it's most effective to build the three fires in a row, as the succession of smoke fumes will signal to anyone who can see them that you need help.

"You want to use anything that's going to cause a lot of smoke" to build the fire, she said, such as green vegetation or damp wood —which produce more smoke because it burns at a lower temperature and results in incomplete combustion .

Depending on the circumstances, it may be best to wait to light the fires until a plane or boat is visible to ensure you are ready when a rescue team is nearby, and so they do not miss you.

Beyond fires, Bigney said it's ideal to create signals with contrast. So in the case of the men stranded on Pikelot Atoll, palm leaves against bright white sand may've created enough contrast to be seen easily from afar.

It's also generally recommended to spell out SOS , rather than a message like "HELP," in part because the letters in SOS can also be read upside down.

"Now what they did, worked," Bigney said of the rescued men. "So I think the take-home message is be creative and do something."

The Coast Guard said the men had access to food and water while stranded with their damaged boat. Bigney said it was not necessarily the most dire survival situation but that other factors, like incessant sand fleas or nearby predators, could make it hard for some people to think clearly.

"They did a good job. They thought to signal for help," she said, adding, "Oftentimes it is just a psychological game, in any situation."

Watch: Maui wildfire survivors return to devastation

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Biden administration sets first-ever limits on ‘forever chemicals’ in drinking water

Logan Feeney pours a PFAS water sample into a container for research, Wednesday, April 10, 2024, at a U.S. Environmental Protection Agency lab in Cincinnati. The Environmental Protection Agency on Wednesday announced its first-ever limits for several common types of PFAS, the so-called “forever chemicals,” in drinking water. (AP Photo/Joshua A. Bickel)

Vials containing PFAS samples sit in a tray, Wednesday, April 10, 2024, at a U.S. Environmental Protection Agency lab in Cincinnati. The Environmental Protection Agency on Wednesday announced its first-ever limits for several common types of PFAS, the so-called “forever chemicals,” in drinking water. (AP Photo/Joshua A. Bickel)

FILE - Environmental Protection Agency Administrator Michael Regan speaks at the University of Maryland on May 11, 2023, in College Park, Md. The Environmental Protection Agency announced, Wednesday, April 10, 2024, its first-ever limits for several common types of PFAS, the so-called “forever chemicals,” in drinking water. (AP Photo/Nathan Howard, File)

Jackson Quinn brings PFAS water samples into a temperature controlled room, Wednesday, April 10, 2024, at a U.S. Environmental Protection Agency lab in Cincinnati. The Environmental Protection Agency on Wednesday announced its first-ever limits for several common types of PFAS, the so-called “forever chemicals,” in drinking water.(AP Photo/Joshua A. Bickel)

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The Biden administration on Wednesday finalized strict limits on certain so-called “forever chemicals” in drinking water that will require utilities to reduce them to the lowest level they can be reliably measured. Officials say this will reduce exposure for 100 million people and help prevent thousands of illnesses, including cancers.

The rule is the first national drinking water limit on toxic PFAS, or perfluoroalkyl and polyfluoroalkyl substances, which are widespread and long lasting in the environment.

Health advocates praised the Environmental Protection Agency for not backing away from tough limits the agency proposed last year . But water utilities took issue with the rule, saying treatment systems are expensive to install and that customers will end up paying more for water.

Water providers are entering a new era with significant additional health standards that the EPA says will make tap water safer for millions of consumers — a Biden administration priority. The agency has also proposed forcing utilities to remove dangerous lead pipes .

FILE - Wisconsin Gov. Tony Evers speaks prior to President Joe Biden's appearance at an event about canceling student debt, Monday, April 8, 2024, at the Madison Area Technical College Truax campus in Madison, Wis. Evers vetoed a Republican bill Tuesday, April 9, that would have created grants to fight pollution from “forever chemicals” and took the unusual step of calling the GOP-controlled budget committee into meeting to approve spending $125 million to deal with contamination. (AP Photo/Kayla Wolf, File)

Utility groups warn the rules will cost tens of billions of dollars each and fall hardest on small communities with fewer resources . Legal challenges are sure to follow.

EPA Administrator Michael Regan says the rule is the most important action the EPA has ever taken on PFAS.

“The result is a comprehensive and life-changing rule, one that will improve the health and vitality of so many communities across our country,” said Regan.

PFAS chemicals are hazardous because they don’t degrade in the environment and are linked to health issues such as low birth weight and liver disease, along with certain cancers. The EPA estimates the rule will cost about $1.5 billion to implement each year, but doing so will prevent nearly 10,000 deaths over decades and significantly reduce serious illnesses.

They’ve been used in everyday products including nonstick pans, firefighting foam and waterproof clothing. Although some of the most common types are phased out in the U.S., others remain. Water providers will now be forced to remove contamination put in the environment by other industries.

“It’s that accumulation that’s the problem,” said Scott Belcher, a North Carolina State University professor who researches PFAS toxicity. “Even tiny, tiny, tiny amounts each time you take a drink of water over your lifetime is going to keep adding up, leading to the health effects.”

PFAS is a broad family of chemical substances, and the new rule sets strict limits on two common types — called PFOA and PFOS — at 4 parts per trillion. Three other types that include GenEx Chemicals that are a major problem in North Carolina are limited to 10 parts per trillion. Water providers will have to test for these PFAS chemicals and tell the public when levels are too high. Combinations of some PFAS types will be limited, too.

Regan will announce the rule in Fayetteville, North Carolina, on Wednesday.

Environmental and health advocates praised the rule, but said PFAS manufacturers knew decades ago the substances were dangerous yet hid or downplayed the evidence. Limits should have come sooner, they argue.

“Reducing PFAS in our drinking water is the most cost effective way to reduce our exposure,” said Scott Faber, a food and water expert at Environmental Working Group. “It’s much more challenging to reduce other exposures such as PFAS in food or clothing or carpets.”

Over the last year, EPA has periodically released batches of utility test results for PFAS in drinking water. Roughly 16% of utilities found at least one of the two strictly limited PFAS chemicals at or above the new limits. These utilities serve tens of millions of people. The Biden administration, however, expects about 6-10% of water systems to exceed the new limits.

Water providers will generally have three years to do testing. If those test exceed the limits, they’ll have two more years to install treatment systems, according to EPA officials.

Some funds are available to help utilities. Manufacturer 3M recently agreed to pay more than $10 billion to drinking water providers to settle PFAS litigation. And the Bipartisan Infrastructure Law includes billions to combat the substance. But utilities say more will be needed.

For some communities, tests results were a surprise. Last June, a utility outside Philadelphia that serves nearly 9,000 people learned that one of its wells had a PFOA level of 235 parts per trillion, among the highest results in the country at the time.

“I mean, obviously, it was a shock,” said Joseph Hastings, director of the joint public works department for the Collegeville and Trappe boroughs, whose job includes solving problems presented by new regulations.

The well was quickly yanked offline, but Hastings still doesn’t know the contamination source. Several other wells were above the EPA’s new limits, but lower than those the state of Pennsylvania set earlier. Now, Hastings says installing treatment systems could be a multi-million dollar endeavor, a major expense for a small customer base.

The new regulation is “going to throw public confidence in drinking water into chaos,” said Mike McGill, president of WaterPIO, a water industry communications firm.

The American Water Works Association, an industry group, says it supports the development of PFAS limits in drinking water, but argues the EPA’s rule has big problems.

The agency underestimated its high cost, which can’t be justified for communities with low levels of PFAS, and it’ll raise customer water bills, the association said. Plus, there aren’t enough experts and workers — and supplies of filtration material are limited.

Work in some places has started. The company Veolia operates utilities serving about 2.3 million people across six eastern states and manages water systems for millions more. Veolia built PFAS treatment for small water systems that serve about 150,000 people. The company expects, however, that roughly 50 more sites will need treatment — and it’s working to scale up efforts to reduce PFAS in larger communities it serves.

Such efforts followed dramatic shifts in EPA’s health guidance for PFAS in recent years as more research into its health harms emerged. Less than a decade ago, EPA issued a health advisory that PFOA and PFOS levels combined shouldn’t exceed 70 parts per trillion. Now, the agency says no amount is safe.

Public alarm has increased, too. In Minnesota, for example, Amara’s Law aims to stop avoidable PFAS use. It’s been nearly a year since the law’s namesake, Amara Strande, died from a rare cancer her family blames on PFAS contamination by 3M near her high school in Oakdale, although a connection between PFAS and her cancer can’t be proven. Biden administration officials say communities shouldn’t suffer like Oakdale. 3M says it extends its deepest condolences to Amara’s friends and family.

Losing Amara pushed the family towards activism. They’ve testified multiple times in favor of PFAS restrictions.

“Four parts per trillion, we couldn’t ask for a better standard,” Amara’s sister Nora said. “It’s a very ambitious goal, but anything higher than that is endangering lives.”

Associated Press data journalist Camille Fassett in San Francisco and reporter Matthew Daly in Washington D.C. contributed to this story.

The Associated Press receives support from the Walton Family Foundation for coverage of water and environmental policy. The AP is solely responsible for all content. For all of AP’s environmental coverage, visit https://apnews.com/hub/climate-and-environment

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Music Features

Can rap beef exist when no one agrees on what's being fought for, j. cole, kendrick lamar and drake in a conflict without a cause.

Sheldon Pearce

J. Cole and Drake perform during the 2023 edition of Cole's Dreamville festival in Raleigh, N.C., last April. Their collaborative track "First Person Shooter" recently touched off a war of words with fellow MC Kendrick Lamar. Astrida Valigorsky/WireImage/Getty Images hide caption

In 2016, then-President Barack Obama weighed in on an issue of utmost national importance: Who would win in a rap battle between Kendrick Lamar and Drake ? "Gotta go with Kendrick," he said. "I think Drake is an outstanding entertainer, but Kendrick, his lyrics ..." It was pretty clear what distance he was covering between "entertainer" and "lyricist"; it's one that has been subject to debate since hip-hop's earliest days. But just as fascinating, to me, is the idea of asking the commander-in-chief such a question in the first place: not simply pitting the two divergent stars against each other in the critical imagination, but supposing that any such showdown could be conclusive — that it could say something substantial about the artists' standing — when even the framing of Obama's answer seems to be partitioning them.

Such a battle may yet come to fruition thanks to Kendrick Lamar, who recently made a surprise appearance on Future and Metro Boomin's " Like That " to take aim at Drake and the other peer with whom they have formed the defining rap triumvirate of the last decade, J. Cole . Kendrick, no stranger to putting others on notice, made plain his distaste for the duo collaborating and asserting their primacy on the chart-topping 2023 single " First Person Shooter ," and decided to shoot back. On Friday, only a few weeks removed from the explosive Kendrick verse, Cole provided his own lukewarm response on the song " 7 Minute Drill ," from a surprise album called Might Delete Later . His fire didn't even last the weekend: During a set at his Dreamville festival, he denounced the song , calling it "the lamest s*** I ever did." "I felt conflicted 'cause I'm like, bruh, I know I don't really feel no way," he continued, "but the world wanna see blood" — in essence admitting he was going through the motions of a ritual he does not believe in.

In recent years, Kendrick, Drake and Cole have been frequently lumped together as a stable of thoroughbreds sometimes known as the "big three," in part because of how their respective rises overlapped across the 2010s. Drake was the first to break through, as a teen-drama bit player turned passive-aggressive love bomber, and his performance of emotional availability and inclination toward pop melody quickly made him a pervasive (and sometimes malignant) presence, consuming everything in his path. As Drake was expanding from Young Money scion to capital asset , Kendrick emerged from the indie-rap giant TDE's Black Hippy supergroup as a world-weary eyewitness powered by a formidable, disarming lyricism; his cinematic debut, good kid, m.A.A.d city , set the stage for a blue-ribbon career, elevated by the intrepid jazz-rap pièce de résistance To Pimp a Butterfly and capped by rap's first-ever Pulitzer Prize win for music. Cole, a small-market rapper-producer studying at St. John's, realized his rap dream by becoming a Jay-Z apprentice , and the striving, parochial focus and withdrawn disposition of his subsequent albums made him a self-tormented rap monk for a devout following of armchair intellectuals.

Every aspect of the recent exchange between these three makes me question who and what beef is for, especially now. There is an obvious thrill in rappers going head-to-head for the sport of it, particularly at this level of visibility, where exposure becomes theater. But in today's game, the terms of the wager feel opaque: None of the participants seem to even be playing by the same rules, much less for the same prize. Any pursuit of rap's invisible throne seems almost immaterial without real matters of succession to be settled. Can such a thing have meaning if the factions don't at least agree on what is being fought for?

Conflicted: Two Battles Illustrate How Hip-Hop Is Fueled By Competition

Even if the gladiatorial spectacle of great competitors duking it out for glory has historically been entertaining and at times career-making, rap beef has usually come with tangible stakes. The Bridge Wars were fought over territory, airplay and rap's birthright. Drake's feud with Meek Mill was about authorship. His feud with Pusha T is an extension of Pusha's feud with Drake's mentor, Lil Wayne, over style. Every Nicki feud — Lil Kim, Remy Ma, Cardi B, Megan Thee Stallion — seems to come back to the Highlander principle that there can only be one successful woman in rap at a time , which has long been dispelled and yet seems to remain primary directive driving her quarrels. Even Jay-Z's shots at Nas on " Takeover " were a response to Nas claiming he was taking Biggie's name in vain, as both men made a play for his vacated title as King of New York. This latest feud isn't predicated on any slight, real or perceived, but instead is largely about positioning — who gets to finally break away from the pack — and yet there is little for any of them to lose, and less to be gained.

Drake and Kendrick are natural foils that play into an established binary, the commercial juggernaut facing down the highbrow philosopher messiah. Neither portrayal is entirely fair: Drake has, on many occasions, displayed not just bars but battle savvy (and a love of the form, co-hosting a King of the Dot rap battle in 2011), and Kendrick spent much of his last album rebuking any attempt to put him on the cross as a lyrical emancipator. Yet there is still something symbolic at play in pitting them against one another. It is hard to imagine a more blatant dividing line for hip-hop morality, and it helps that they are the most celebrated rappers of their era, if by vastly different measures.

Cole has often felt like the odd man out in this conversation. Neither hitmaker nor auteur, his inclusion in the so-called "big three" seems to be out of respect for the relevance he enjoys via a reverent fanbase, but his limitations stand out when compared directly to his peer group. What's more, even though it was in part his invocation of the "big three" on "First Person Shooter" that started all this upset, he has never had the disposition for the pugnaciousness and scheming of rap kingmaking. Drake is petty and has never met a dig he couldn't take to the grave. Kendrick is proud and has never met a challenge he couldn't take personally. Both modes lend themselves not only to settling gripes in the open, but the shoulder-checking required to emerge atop a hip-hop scrum. Even releasing his diss on a project called Might Delete Later implies an apprehension in Cole that simply does not fit the format.

Microphone Check

J. cole on competition and writing honest songs.

The Blast Radius Of Kendrick Lamar's 'Control' Verse

As declarations of war, "Like That" and "7 Minute Drill" could not be further from one another, and each one says a lot about the rapper who made it, perhaps more so now that Cole has backed down. On "Like That," the barbs are strung together like razor wire, and there is a charge that runs through it, the exhilaration of getting something off your chest. Kendrick is teeming with energy, on the front foot, making a case for his skill as singular and undercutting Drake's advantage in the process. "I'm really like that / And your best work is a light pack / N****, Prince outlived Mike Jack," he raps, nodding to Drake's recent move into a dead heat with the King of Pop for most Hot 100 No. 1 singles. (It is clear that most of his vitriol is for Drake, with whom he has traded jabs for many years.) The verse was effective in riling up the internet, enough to pressure a response from Cole, and it's hard to argue against its potency, but it's unclear what is being accomplished. Unlike his scorched-earth verse on Big Sean's " Control ," where Kendrick was an upstart staking claim to an authority that had yet to materialize but felt inevitable, "Like That" does not say anything his work hasn't already said for him, and it cannot cut into the market cap of his competitors. He also has not changed the state of play, as Pusha T once did. He sidesteps detailed talking points for undermining both artists to come at them straight on, which is more in line with a move to maintain the status quo than to shake things up.

"7 Minute Drill" is the response it deserves — halfhearted and full of pulled punches. The song and its subsequent disavowal are befitting of the rapper who made " Pride Is the Devil ," one wrestling with the pressure of his ego at all times. Cole directly cites "Takeover," critiquing the Kendrick album arc (and TPAB , specifically) by reusing Jay's blueprint: "Four albums in 12 years, n****, I can divide." There was never a path to victory in this for Cole; the worst Kendrick album, whatever it may be, is better than the best Cole album. His has always been an underdog story, underscored by the "platinum with no features" mantra, and to play to that persona in this situation is a tacit acknowledgment that he is punching above his weight. In this way, Cole recusing himself from the clash feels less like some damning confirmation that he is not The One, and more like proof that he and his opponents are simply after different things. And it underscores something that fan accounting of rap beefs tends to overlook: The case for your legacy is made by your music in its totality, no matter what you do in battle. After all, Jay-Z, the one being cited for his landmark offensive against Nas, is recognized by many (myself included) as the greatest rapper ever — and he lost that fight.

Each member of the "big three" has, at one time or another, revealed themselves to be students of Jay's tactics; though Nas' " Ether " ruled the day back in 2001, "Takeover" has held as a defining document for them. Kendrick once said that the latter was better because Jay was "saying more facts." I'd say it's more that it makes you believe a fiction — that it carefully rewrites the Nas narrative to suit Jay's ends. You can hear Kendrick's obsession with "facts" in the upfront "Like That" verse; Cole clearly shares the ethic in his bending of the truth; and the debater's methodology surfaces in a Drake diss like " Duppy Freestyle ." But their paths diverge in their adoption of this gospel, as each seems to have a different Hovism running in their mind. For Cole, it is " I hear you baiting me lately , I been doing my best just to stay hater-free / Still, watch what you say to me," as the ease-seeking, reluctant combatant. For Kendrick: " Don't talk to me 'bout MCs got skills / He's all right, but he's not real," as the obsessive heir apparent. For Drake: " Men lie, women lie, numbers don't ," as the all-consuming data machine in a time when fans wield figures like a cudgel. One seeks success in solitude, one in culture-shifting, one in quantifiable ubiquity. Militancy suits Kendrick best, and to play by those rules is to skew the elusive target at the center of rap discourse in his favor.

That's why it is hardest to imagine what Drake might get out of all of this if it continues into a second phase. He has yet to respond, but it feels as if any response would only diminish him. Drake is too big to fail , a chart certainty at this point, and he will likely never be "rap" enough for those who really value this kind of exercise. Unlike previous Drake foes, Kendrick is the only rapper in his class who isn't dwarfed by his numbers, and thus presents a true challenge for a shrewd competitor. Why take the risk for no reward? There is no cachet to be earned in such a clash because, as every Drake album this decade makes clear, he has no interest in playing the prestige game anymore. His security lies in his sheer undeniability — an armor that even a perception-shifting diss labeling him a self-hating deadbeat dad couldn't pierce. This even being a conversation must be as unfathomable to him as it is to Kendrick.

If that feels like an anticlimactic outcome for hip-hop's reigning titans squaring off, blame it on the context-flattening effect of our current social reality. Rap purists and pop number-crunchers are all wading around in the same murky discourse soup, and a lot of the metrics that used to feel like a given are looser now, superimposing a shape on something gray and amorphous. It figures, then, that the truest means of ascent is to try and to get to the top of the trending list, and to stay as long as possible; the only thing of equal value to everyone is collective attention. The diehards will never admit it, but beef has become far more about the drama than the bars or even the hostility, which explains both the overenthusiasm for a pretty down-the-middle shot from Kendrick, and the sourpuss complaints about Cole's retraction. Watching the video of that apology, I can't stop thinking about his characterization of beef as a vicious spectacle: "The world wanna see blood ." In that context, you can think of the "Like That" verse as chum in the tank, bait that hooked Cole into a squabble he didn't even really want. Maybe if there was something that actually needed hashing out, it'd be worth it.

Keep Listening

Stream a playlist of the songs (and beefs) referenced in this article.

Kendrick Lamar

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How to Write a Strong Hypothesis
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. If a first-year student starts attending more lectures, then their exam scores will improve.
How to Write a Hypothesis in 6 Steps, With Examples
5 Logical hypothesis. A logical hypothesis suggests a relationship between variables without actual evidence. Claims are instead based on reasoning or deduction, but lack actual data. Examples: An alien raised on Venus would have trouble breathing in Earth's atmosphere. Dinosaurs with sharp, pointed teeth were probably carnivores. 6 Empirical ...
5.2
5.2 - Writing Hypotheses. The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis ( H 0) and an alternative hypothesis ( H a ). When writing hypotheses there are three things that we need to know: (1) the parameter that we are testing (2) the ...
How to Write a Strong Hypothesis
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. If a first-year student starts attending more lectures, then their exam scores will improve.
How to Write a Hypothesis w/ Strong Examples
How to Write a Good Hypothesis. Writing a good hypothesis is definitely a good skill to have in scientific research. But it is also one that you can definitely learn with some practice if you don't already have it. Just keep in mind that the hypothesis is what sets the stage for the entire investigation. It guides the methods and analysis.
How to Write a Strong Hypothesis in 6 Simple Steps
Learning how to write a hypothesis comes down to knowledge and strategy. So where do you start? Learn how to make your hypothesis strong step-by-step here.
How to Write a Hypothesis
Use simple language: While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording. State direction, if applicable: If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this.
How to Write a Hypothesis 101: A Step-by-Step Guide
Step 3: Build the Hypothetical Relationship. In understanding how to compose a hypothesis, constructing the relationship between the variables is key. Based on your research question and variables, predict the expected outcome or connection.
How to Write a Hypothesis (Steps & Examples)
Here are the types of hypothesis you should know as a writer. 1. "Null" Hypothesis: Says there's no connection between things. 2. "Alternative" Hypothesis: Says there is a connection between things. 3. "Simple" Hypothesis: Predicts how one thing affects another. 4.
How to Write a Hypothesis
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.
The Craft of Writing a Strong Hypothesis
Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4.
Formulating Strong Hypotheses
There are some important things to consider when building a compelling, testable hypothesis. Clearly state the prediction you are proposing. Make sure that the hypothesis clearly defines the topic and the focus of the study. Mask wearing and its effect on virus case load. Aim to write the hypothesis as an if-then statement.
Hypotheses
How to Write a Hypothesis. A hypothesis is a statement. Avoid conditional terms like should , might or could. A hypothesis can be phrased in an if/then format, Ex. if you use Topical Treatment A for male pattern baldness, then you will see a 50% increase in hair grown within 3 months. Another workable structure is when x, then y.
What is and How to Write a Good Hypothesis in Research?
An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions. Use the following points as a checklist to evaluate the effectiveness of your research hypothesis: Predicts the relationship and outcome.
How to Write a Great Hypothesis
What is a hypothesis and how can you write a great one for your research? A hypothesis is a tentative statement about the relationship between two or more variables that can be tested empirically. Find out how to formulate a clear, specific, and testable hypothesis with examples and tips from Verywell Mind, a trusted source of psychology and mental health information.
5 Rules for Creating a Good Research Hypothesis
In this article, we will focus on writing your hypothesis. Five rules for a good hypothesis. 1: A hypothesis is your best guess about what will happen. A good hypothesis says, "this change will result in this outcome." The "change" is a variation on an element—a label, color, text, etc.
How to Write a Hypothesis: 13 Steps (with Pictures)
1. Select a topic. Pick a topic that interests you, and that you think it would be good to know more about. [2] If you are writing a hypothesis for a school assignment, this step may be taken care of for you. 2. Read existing research. Gather all the information you can about the topic you've selected.
What Is a Hypothesis and How Do I Write One?
Merriam Webster defines a hypothesis as "an assumption or concession made for the sake of argument.". In other words, a hypothesis is an educated guess. Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it's true or not.
How to Write a Hypothesis in 5 Easy Steps:
How to Write a Hypothesis: A STEP-BY-STEP GUIDE. Ask a Question. The starting point for any hypothesis is asking a question. This is often called the research question. The research question is the student's jumping-off point to developing their hypothesis. This question should be specific and answerable.
Writing Hypotheses
How to conduct a hypothesis test. How to conduct a hypothesis test. Writing Hypotheses. We can write hypotheses for a single mean (µ), paired means(µd), a single proportion (p), the difference between two independent means (µ1-µ2), the difference between two proportions (p1-p2), a simple linear regression slope (β), and a correlation (ρ).
Lab 1 Principles of Scientific Inquiry and the Scientific Method in the
What are the rules for writing the hypothesis? When you write a hypothesis, there are a few key steps that need to be remembered for writing. First, there is never a "good" or a "bad" hypothesis, just one that is not well written. Second, there is never a "right" or a "wrong" hypothesis, just one that gets supported (shown to be ...
How to Write a Null Hypothesis (5 Examples)
Whenever we perform a hypothesis test, we always write a null hypothesis and an alternative hypothesis, which take the following forms: H0 (Null Hypothesis): Population parameter =, ≤, ≥ some value. HA (Alternative Hypothesis): Population parameter <, >, ≠ some value. Note that the null hypothesis always contains the equal sign.
The universal rule of three
Kelsey Vlamis. Apr 16, 2024, 1:10 PM PDT. Two of three men stranded on the uninhabited island of Pikelot Atoll in Micronesia wave life jackets as a U.S. Navy P-8A Poseidon maritime patrol and ...
EPA sets first-ever limits on PFAS in water
The rule is the first national drinking water limit on toxic PFAS, or perfluoroalkyl and polyfluoroalkyl substances, which are widespread and long lasting in the environment. Health advocates praised the Environmental Protection Agency for not backing away from tough limits the agency proposed last year. But water utilities took issue with the ...
J. Cole, Kendrick Lamar and Drake's rap beef without a cause : NPR
If the current conflict between J. Cole, Kendrick Lamar and Drake feels confusing, it's because the artists often hailed as hip-hop's "big three" have never played by the same rules.
Need more time to file a federal tax return? It's easy with IRS Free
Special rules offer some taxpayers more time without having to request an extension: U.S. citizens and resident aliens who live and work outside of the United States and Puerto Rico get an automatic two-month extension, until June 15, to file their tax returns. However, tax payments are still due April 15 or interest will accrue on the unpaid tax.

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How to Write a Strong Hypothesis | Steps & Examples

Example: Hypothesis

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Variables in hypotheses

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Step 1. Ask a question

Step 2. Do some preliminary research

Step 3. Formulate your hypothesis

4. Refine your hypothesis

5. Phrase your hypothesis in three ways

6. Write a null hypothesis

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How to Write a Strong Hypothesis | Guide & Examples

Table of contents

Variables in hypotheses

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Step 2: Do some preliminary research

Step 3: Formulate your hypothesis

Step 4: Refine your hypothesis

Step 5: Phrase your hypothesis in three ways

Step 6. Write a null hypothesis

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

Introduction

What is the simple definition of a hypothesis?

What is the hypothesis for in research?

What is an example of a hypothesis?

What makes a good hypothesis?

Null hypothesis

Alternative hypothesis

Simple hypothesis

Complex hypothesis

Directional hypothesis

Statistical hypothesis

Empirical hypothesis

Causal hypothesis

Associative hypothesis

Relational hypothesis

Logical hypothesis

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Literature review

Research methods

Preliminary research

Data analysis

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Learn How To Write A Hypothesis For Your Next Research Project!

What is a Hypothesis?

The Building Blocks of a Hypothesis

Types of Hypotheses

Alternative Hypothesis (H1)

Null Hypothesis (H0)

Importance of a Hypothesis in Research

How to Write a Hypothesis?

Step 1: Identify Your Research Question

Step 2: Determine the Variables

Step 3: Build the Hypothetical Relationship

Step 4: Write the Null Hypothesis

Step 5: Review Your Hypothesis

Hypothesis vs. Prediction: What’s the Difference?

Hypothesis: The Foundation of Your Research

Prediction: The Expected Outcome

Key Differences Between a Hypothesis and a Prediction

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How to Write a Hypothesis – Steps & Tips

What is a Research Hypothesis?

Research Hypothesis Definition

Variables in Hypothesis

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

Step 1: Start with a Research Question

Step 2: Do Preliminary Research

Step 3: Define Variables