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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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. It is a preliminary answer to your question that helps guide the research process.

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

How to Write a Hypothesis

What is a hypothesis.

A hypothesis is your initial prediction about your topic or argument. Although you’re probably used to writing hypotheses in science, you can also use them effectively in other areas of research. Why Start With a Hypothesis?

When researching, creating a hypothesis gives you a place to start from. It helps you frame your research and know what to look for. Sometimes, your research question is just too big. When you start with a hypothesis, it can help you narrow your scope and figure out what information to focus on.

For example, instead of starting with the topic of the United States, which is very broad and may have too much information, you might choose the thesis “The United States almost lost the Revolutionary War,” which would help you narrow your search to information on the American Revolution.

What Should a Hypothesis Look Like?

You shouldn’t worry about creating a hypothesis that is right or wrong. It’s just a prediction! As you research, you will find out if your guess was correct.

As you write your hypothesis, make sure that it:

• Relates to the topic
• Uses higher order thinking
• Looks like an argument

Each of the hypotheses below relate to the question:

“What would the United States be like if we never fought the Revolutionary War?”

There are a lot of possible answers to this question. A hypothesis will help you focus on specific pieces of information.

Beginning Your Research: Identify the Information You Need

Once you have a hypothesis, you can identify what information you need to find out. Most likely, you will need to find data and evidence related to your prediction. This evidence may support your prediction, or it may prove it wrong; both are okay!  The point of research is to learn, not to be right.

If your hypothesis is, “The United States would be a much smaller and less diverse nation if we never fought the Revolutionary War,” some of the information you will need to gather includes:

• ​Statistics on population and diversity before the war and today
• Specific examples of how fighting the war did or did not lead to greater diversity
• Specific examples of how fighting the war did or did not lead to the nation growing

If you can’t find the information you need to support your hypothesis, that’s okay! You can adjust your hypothesis as you gather information and learn more about the topic.

Creating a hypothesis is helpful and will be the central theme of your project. Don’t be afraid to explore different options before deciding on one that you like the most. — As you research, it’s ethical to build a bibliography to keep track of the sources you use to support your hypothesis. Easily make one in MLA format , APA format , Chicago, or more with BibMe citation tools. Our premium BibMe Plus service also offers a grammar check to help you improve your writing. Try it today!

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Methodology

• 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.

Table of contents

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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Helping you do great citizen science

Writing Hypotheses

By: Dr. Stacy Lynn , Natural Resource Ecology Lab, Colorado State University

*Special thanks to Dr. Paul Strode and Dr. Cecilia Hennessy for encouraging us to update and improve this piece.

Developing a hypothesis is a natural step in scientific inquiry, generally follows the development of a research question and functions as a tentative answer to the question. Some citizen science studies are more observational and not necessarily focused on a research question (e.g., document all species of butterflies observed in my neighborhood), but many studies do take observations a step further to ask a research question that relates those observations to changes in another variable that could be influencing patterns (e.g., plant species diversity or prevalence of native species on different properties in the neighborhood). For such studies, a hypothesis is essentially a specific, tentative, testable, measurable and falsifiable statement made prior to your research. There are two types of scientific hypotheses: 

• Generalizing hypotheses: This pattern exists, and with data it might become a future law or relationship.
• Explanatory hypotheses: This mechanism causes this pattern and might become a future theory.

Your hypotheses will be dependent on the conditions under which variables are being measured and the methods you are using to collect your data (Strode 2020).

Developing a well-constructed hypothesis can help guide you to an appropriate research design for your project. If your research question and hypothesis are not well-defined you may have trouble coming up with a project design to test your hypothesis. Alexander Toledo, a PhD in Biomedical Sciences and expert in developing scientific experiments that use the scientific method, claims a hypothesis is crucial for a sound and well-developed experiment and emphasizes how the hypothesis should contribute to the solution or answer to the research question (Toledo et al 2011).

How do data collected by volunteers contribute to addressing my hypothesis?

The goal of your experiment or field study should never be to prove your hypothesis right (or another one wrong), because having such an agenda may impart bias into your approach. Rather, the goal of a scientific experiment is to learn through a structured process more about how the world works. Hypotheses should remain neutral in an effort to keep science as objective as possible. Make sure your hypothesis is capable of being supported or unsupported by the data that you and your participants collect (Cohen 2013). 

How do I write a good hypothesis?

A good hypothesis should guide your research and narrow down the variables of interest and the relationship of interest to one that is measurable. Each research question and related hypothesis should address the relationship between one independent variable and one dependentvariable. If there are multiple dependent variables, these should be set up as individual sub-hypotheses (see example #3 below. An independent variable is one which the scientist researcher (you!) changes or manipulates (such as soil amendments or total water applied), or which occurs in categories (such as mid-summer vs. mid-winter, or various distance intervals) or along a natural gradient (such as temperature or rainfall means). A dependent variable is one which you are measuring the values of relative to changes in the independent variable.

Below are a few examples of hypothesis improvements to get you started:

How do I know I have a strong hypothesis?

My hypothesis is related to my research question.

The hypothesis should present a description of a pattern or relationship between two variables that can be further supported or rejected based on the data to be collected during the research.

My hypothesis is specific, and there is a 1:1 relationship between my research questions and hypotheses

Every research question should have its own hypothesis. If a hypothesis has a list of expected outcomes, then think about splitting the research question either into multiple questions, or to list sub-components that can be associated with an independent hypothesis, and easily be tested independent of each other.

My hypothesis is falsifiable

Ask yourself two questions:

• Did I choose/design a data collection protocol that will generate data to address my research question?
• Do I have a clearly identifiable approach to collect and analyze data?

Testing my hypothesis is achievable

I have access to the resources I need to collect my data.

If I need special permissions, tools or other considerations to collect data, I’ve figured out how to get them.

My hypothesis is neutral.

I’ve done my best to avoid inherent biases and leading language when asking and answering my research question.

The purpose of my experiment is to objectively look for a relationship between two variables

My hypothesis is simple and clear.

If project participants need any prior knowledge, training or equipment to collect data, I’ve given thought to how to provide it: what form resources need to be in to best serve their needs and be accessible to a diverse group of participants.

I know how I will analyze the data we collect.

A Quick Word about Causation, Correlation, and Sample Sizes

When you observe correlation between independent and dependent variables, it can be tempting to assume the relationship is causal (that one variable causes the other). Be careful not to fall into this trap! Correlation does not necessarily mean that the relationship is causal. There could be something else at work that influences the relationship. Determining causation can take multiple studies that consider multiple potential alternative explanations and control for those.

Sample size is also an important consideration as you plan your study. The more data you collect, the more reflective your sample and results are likely to be of reality. A small sample size – just a few observations – is “observational” and not enough to support or refute your hypothesis to answer your research question(s). Generally, the larger the number of measurements observed, the more observations you need to make to identify patterns.

Making citizen science place-based and relevant

Citizen science is often naturally “place-based”, or rooted in a local perspective. Even when a citizen science project is interested in larger scale than the local, your participants will be connecting with the local processes in the place where they live and/or collect data. So larger projects may string together data from many localities, each of which is grounded in the local context, to see what the relationship between variables is over space and/or time.

Creating projects that are founded on local issues and circumstances that are relevant to your anticipated participants and which may potentially have impacts on land use, management, or policy decision-making can bring both enthusiasm and a sense of making a difference (Newman et al 2016). Volunteers naturally like to make a difference with their time and efforts, and learning something new fits into the model of lifelong learning that draws many people to citizen science. Volunteers learn something new while contributing to a greater group effort that would not be possible without their contributions and the contributions of many others.

How do I handle unanticipated results?

Remember, it’s okay if your data and results are unanticipated and do not support your hypothesis. All results are interesting! If the results of your study are not what you expected, that could generate more questions for you to answer. Continue to build your data, and over time you will be able to narrow down the relationship between your variables of interest. It is also possible to run repeat studies multiple times over time, and to look at patterns in the data over seasons and years, and a variety of different conditions (e.g., total precipitation, “shock” events such as wildfire, etc.).

Important Takeaways

The purpose of a hypothesis is to voice your expectation of what you will find as the relationship between two variables, based on knowledge and previous experience.

A good hypothesis is one that is specific, tentative, testable, measurable, and falsifiable.

A good research project design (either experimental or observational) is Specific, Measurable, Achievable, Relevant, and Time-bound.

When research results do not align with your hypothesis, that is interesting too!

Cohen, M. F. (2013). An introduction to logic and scientific method. Read Books Ltd.

Newman, G., M. Chandler, M. Clyde, B. McGreavy, M. Haklay, H. Ballard, S. Gray, R. Scarpino, R. Hauptfeld, D. Mellor, J. Gallo (2016). Leveraging the power of place in citizen science for effective conservation decision-making. Biological Conservation. 10pp. htpp://dx.doi.org/10.1016/j.biocon.2016.07.019.

Strode, Paul. (2020). Science: It’s Not Always Hypothetical. The American Biology Teacher. 82(8): 513. https://doi.org/10.1525/abt.2020.82.8.513

Teaching the Hypothesis . Paul K. Strode, November 2, 2014

The Writing Center (2017) How to Write a Research Question,  George Mason University, https://writingcenter.gmu.edu/guides/how-to-write-a-research-question

Toledo, A. H., Flikkema, R., and L. H. Toledo-Pereyra (2011). Developing the Research Hypothesis. Journal of Investigative Surgery, 24:5, 191-194, DOI: 10.3109/08941939.2011.609449  https://doi.org/10.3109/08941939.2011.609449

Cover Photo: Sarah Newman

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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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The Scientific Method/Introduction to Science

• 2 Precepts of the Method
• 3.1 Example: Newton and Einstein
• 3.2 The Complete Method

Science [ edit | edit source ]

Modern science is broken into so many divergent branches that it's almost inconceivable to think that they are all related. However, despite the varied subject matter, all scientific disciplines are tied together through their use of a common method, the scientific method. The scientific method is mostly a philosophical exercise that is used to refine human knowledge.

Precepts of the Method [ edit | edit source ]

Different disciplines may employ the general scientific method in slightly different ways, but the major precepts are the same:

Stages of the Method [ edit | edit source ]

We will get into more detail in the following chapters, but the basic steps to the scientific method are as follows:

• Observe a natural phenomenon
• Make a hypothesis about the phenomenon
• Test the hypothesis

Once the hypothesis has been tested, if it is true we can work to find more evidence, or we can find counter-evidence. If the hypothesis is false, we create a new hypothesis and try again.

The important thing to note here is that the scientific process is never-ending. No result is ever considered to be perfect, and at no point do we stop looking at evidence.

Example: Newton and Einstein [ edit | edit source ]

Isaac Newton, a brilliant physicist, developed a number of laws of motion and mechanics that we still use today. For many many years the laws of Newton were considered to be absolute fact. Many years later, a physicist known as Albert Einstein noticed that in certain situations Newton's laws were incorrect. Especially in cases where the object under consideration is moving at speed nearing the speed of light.Einstein helped to create a new theory, the theory of relativity, that corrected those errors. Even though Einstein was a brilliant scientist, modern physicists are developing new theories because there are some small errors in Einstein's theories. Each new generation of physicists helps to reduce the errors of the previous generations.

The Complete Method [ edit | edit source ]

The complete scientific method, as it is generally known is:

• Define the question
• Gather data and observations
• Form hypothesis
• Perform experiment and collect data
• Interpret data and draw conclusions

Notice that the first step is to define the question. In other words, we can not look for an answer if we do not know what the question is first. Once we have the question, we need to observe the situation and gather appropriate data. We need to gather all data, not just selectively acquire data to support a particular hypothesis, or to make analysis more simple.

Once we have our data, we can analyze it to determine a hypothesis. In many cases a hypothesis is a mathematical relationship between the data points. However, it is not necessary to use mathematics at any point with the scientific method. Once we have our hypothesis, we need to test it. Testing is a complicated process, and will be the focus of the second section in this book. We collect data from our tests, and attempt to fit that data to our hypothesis. At this point, we need to ask, is the hypothesis right or wrong? Or, if it is not completely wrong nor completely right, we need to ask if this hypothesis is better then the previous hypothesis? If this hypothesis is not quite right, we can modify it and perform the tests again.

Once we have completed tests and verified our hypothesis, we need to draw conclusions from that. What does our hypothesis mean, in the bigger picture? What kinds of relationships between the data can we find? What further problems does this hypothesis cause? What would it take to prove this hypothesis wrong?

• Book:The Scientific Method

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What Is a Testable Hypothesis?

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A hypothesis is a tentative answer to a scientific question. A testable hypothesis is a  hypothesis that can be proved or disproved as a result of testing, data collection, or experience. Only testable hypotheses can be used to conceive and perform an experiment using the scientific method .

Requirements for a Testable Hypothesis

In order to be considered testable, two criteria must be met:

• It must be possible to prove that the hypothesis is true.
• It must be possible to prove that the hypothesis is false.
• It must be possible to reproduce the results of the hypothesis.

Examples of a Testable Hypothesis

All the following hypotheses are testable. It's important, however, to note that while it's possible to say that the hypothesis is correct, much more research would be required to answer the question " why is this hypothesis correct?" 

• Students who attend class have higher grades than students who skip class.  This is testable because it is possible to compare the grades of students who do and do not skip class and then analyze the resulting data. Another person could conduct the same research and come up with the same results.
• People exposed to high levels of ultraviolet light have a higher incidence of cancer than the norm.  This is testable because it is possible to find a group of people who have been exposed to high levels of ultraviolet light and compare their cancer rates to the average.
• If you put people in a dark room, then they will be unable to tell when an infrared light turns on.  This hypothesis is testable because it is possible to put a group of people into a dark room, turn on an infrared light, and ask the people in the room whether or not an infrared light has been turned on.

Examples of a Hypothesis Not Written in a Testable Form

• It doesn't matter whether or not you skip class.  This hypothesis can't be tested because it doesn't make any actual claim regarding the outcome of skipping class. "It doesn't matter" doesn't have any specific meaning, so it can't be tested.
• Ultraviolet light could cause cancer.  The word "could" makes a hypothesis extremely difficult to test because it is very vague. There "could," for example, be UFOs watching us at every moment, even though it's impossible to prove that they are there!
• Goldfish make better pets than guinea pigs.  This is not a hypothesis; it's a matter of opinion. There is no agreed-upon definition of what a "better" pet is, so while it is possible to argue the point, there is no way to prove it.

How to Propose a Testable Hypothesis

Now that you know what a testable hypothesis is, here are tips for proposing one.

• Try to write the hypothesis as an if-then statement. If you take an action, then a certain outcome is expected.
• Identify the independent and dependent variable in the hypothesis. The independent variable is what you are controlling or changing. You measure the effect this has on the dependent variable.
• Write the hypothesis in such a way that you can prove or disprove it. For example, a person has skin cancer, you can't prove they got it from being out in the sun. However, you can demonstrate a relationship between exposure to ultraviolet light and increased risk of skin cancer.
• Make sure you are proposing a hypothesis you can test with reproducible results. If your face breaks out, you can't prove the breakout was caused by the french fries you had for dinner last night. However, you can measure whether or not eating french fries is associated with breaking out. It's a matter of gathering enough data to be able to reproduce results and draw a conclusion.
• What Are Examples of a Hypothesis?
• What Are the Elements of a Good Hypothesis?
• What Is a Hypothesis? (Science)
• How To Design a Science Fair Experiment
• Understanding Simple vs Controlled Experiments
• Scientific Method Vocabulary Terms
• Hypothesis, Model, Theory, and Law
• Null Hypothesis Definition and Examples
• Six Steps of the Scientific Method
• What 'Fail to Reject' Means in a Hypothesis Test
• Scientific Method Flow Chart
• Null Hypothesis Examples
• What Is an Experiment? Definition and Design
• Scientific Hypothesis Examples
• The Visible Spectrum: Wavelengths and Colors

Hypotheses and Proofs

In this post

What is a hypothesis?

A hypothesis is basically a theory that somebody states that needs to be tested in order to see if it is true. Most of the time a hypothesis is a statement which someone claims is true and then a series of tests are made to see if the person is correct.

Hypothesis – a proposed true statement that acts as a starting point for further investigation.

Devising theories is how all scientists progress, not just mathematicians, and the evidence that is found must be collected and interpreted to see if it gives any light on the truth in the statement. Statistics can either prove or disprove a theory, which is why we need the evidence that we gather to be as close to the truth as possible: so that we can give an answer to the question with a high level of confidence.

Hypotheses are just the plural of a single hypothesis. A hypothesis is the first thing that someone must come up with when doing a test, as we must initially know what it is we wish to find out rather than blindly going into carrying out certain surveys and tests.

Some examples of hypotheses are shown below:

• Britain is colder than Spain
• A dog is faster than a cat
• Blondes have more fun
• The square of the hypotenuse of a triangle is equal to the sum of the squares of the other two sides

Obviously, some of these hypotheses are correct and others are not. Even though some may look wrong or right we still need to test the hypothesis either way to find out if it is true or false.

Some hypotheses may be easier to test than others, for example it is easy to test the last hypothesis above as this is very mathematical. However, when it comes to measuring something like ‘fun’ which is shown in the hypothesis ‘Blondes have more fun’ we will begin to struggle! How do you measure something like fun and in what units? This is why it is much easier to test certain hypotheses when compared with others.

Another way to come up with a hypothesis is by doing some ‘trial and error’ type testing. When finding data you may realise that there is in fact a pattern and then state this as a hypothesis of your findings. This pattern should then be tested using mathematical skills to test its authenticity. There is still a big difference between finding a pattern in something and finding that something will always happen no matter what. The pattern that is found at any point may just be a coincidence as it is much harder to prove something using mathematics rather than simply noticing a pattern. However, once something is proved with mathematics it is a very strong indication that the hypothesis is not only a guess but is scientific fact.

A hypothesis must always:

• Be a statement that needs to be proven or disproven, never a question
• Be applied to a certain population
• Be testable, otherwise the hypothesis is rather pointless as we can never know any information about it!

There are also two different types of hypothesis which are explained here:

An Experimental Hypothesis –  This is a statement which should state a difference between two things that should be tested. For example, ‘Cheetahs are faster than lions’.

A Null Hypothesis –  This kind of hypothesis does not say something is more than another, instead it states that they are the same. For example, ‘There is no difference between the number of late buses on Tuesday and on Wednesday’.

Subjects and samples

We have already talked in an earlier lesson of different types of samples and how these are formed, so we will not dwell for too long on this. The main thing to make sure of when choosing subjects for a test is to link them to the hypothesis that we are looking into. This will then give a much better data set that will be a lot more relevant to the questions we are asking. There is no point in us gathering data from people that live in Ireland if our original hypothesis states something about Scottish people, so we need to also make sure that the sample taken is as relevant to the hypothesis as possible. As with all samples that are taken, there should never be any bias towards one subject or another (unless we are using something like quota sampling as outlined in an earlier lesson). This will then mean that a random collection of subjects is taken into account and will mean that the information that is acquired will be more useful to the hypothesis that we wish to look at.

The experimental method

By treating the hypothesis and the data collection as an experiment, we should use as many scientific methods as possible to ensure that the data we are collecting is very accurate.

The most important and best way of doing this is the  control of variables . A variable is basically anything that can change in a situation, which means there are a lot in the vast majority as lots of different things can be altered. By keeping all variables the same and only changing the ones which we wish to test, we will get data that is as reliable as possible. However, if variables are changed that can affect an outcome we may end up getting false data.

For example, when testing ‘A cheetah is faster than a lion’ we could simply make the two animals run against each other and see which is quickest. However, if we allowed the cheetah to run on flat ground and made the lion run up hill, then the times would not be accurate to the truth as it is much harder to run up a slope than on flat ground. It is for this reason that any variables should be the same for all subjects.

The only variable that is mentioned in the hypothesis ‘A cheetah runs faster than a lion’ is the animal that runs. Therefore, this is called the  independent variable  and is the only thing that we wish to change between experiments as it is the thing we wish to  prove has an effect on other results.

A  dependent variable  is something that we wish to measure in experiments to see if there is an effect. This is the speed at which something runs in our example, as we are changing the animal and measuring the speed.

Independent variable – something that stands alone and is not changed by other variables in the experiment. This variable is changed by the person carrying out the investigation to see if it influences the dependent variables. This can also be seen as an input when an experiment is created.

Dependent variable – this variable is measured in an experiment to see if it changes when the independent variable is changed. These represent an output after the experiment is carried out.

Standardised instructions

Another thing that is essential to carrying out experiments is to give both of the participants the same instructions in what you wish them to do. Although this may seem a little picky, there will be a definite difference in how a subject performs if they are given clear and concise instructions as opposed to given misleading and rushed ones.

Turning data into information

Experiments are carried out to produce a set of data but this is not the end of the problem! We will then need to interpret and change this information into something that will tell us what we need to know. This means we need to turn data in the form of numbers into actual information that can be useful to our investigation. Figures that are found through experiments are first shown as ‘raw data’ before we can use different tables and charts to show the patterns that have been found in the surveys and experiments that have been carried out. Once all the data is collected and in tables we can move on to using these to find patterns.

Once a hypothesis has been stated, we can look to prove or disprove it. In mathematics, a proof is a little different to what people usually think. A mathematical proof must show that something is the case without any doubt. We do this by working through step-by-step to build a proof that shows the hypothesis as being either right or wrong. Each small step in the proof must be correct so that the entire thing cannot be argued.

Setting out a proof

Being able to write a proof does not mean that you must work any differently to how you would usually answer a question. It simply means that you must show that something is the case. Questions on proofs may ask you to ‘prove’, ‘verify’ or ‘check’ a statement.

When doing this you will need to first understand the hypothesis that has been stated. Look at the example below to see how we would go about writing a simple proof.

Prove that 81 is not a prime number.

Here we have a hypothesis that 81 is not prime. So, to prove this, we can try to find a factor of 81 that is not 1 as we know the definition of a prime number is that it is only divisible by itself and 1. Therefore, we could simply show that:

The fact that 81 divided by 9 gives us 9 proves the hypothesis that 81 is not prime.

A proof for a hypothesis does not have to be very complex – it simply has to show that a statement is either true or false. Doing this will use your problem-solving skills though, as you may need to think outside the box and ensure that all of the information that you have is fully understood.

Harder examples

Being able to prove something can be very challenging. It is true that some mathematical equations are still yet to be proved and many mathematicians work on solving extremely complex proofs every day.

When looking at harder examples of proofs you will need to find like terms in equations and then think about how you can work through the proof to get the desired result.

Here we need to use the left-hand side to get to the right-hand side in order to prove that they are equal. We can do this by expanding the brackets on the left and collecting the like terms:

We have now expanded the brackets and collected the like terms. It is now that we will need to look at our hypothesis again and try to make the above equation into the right-hand side by moving terms around. We can see from the right-hand side of our hypothesis that we have a double bracket and then 2 added to this so we can begin by bringing 2 out of the above:

So we have now worked through an entire proof from start to finish. Here it is again using only mathematics and no writing:

In the above we have shown that the hypothesis is true by working through step-by-step and rearranging the equation on the left to get the one on the right.

The step-by-step approach to proofs

To prove something is correct we have used a step-by-step approach so far. This method is a very good way to get from the left-hand side of an equation to the right-hand side through different steps. To do this we can use specific rules:

1) Try to multiply out brackets early on where possible.  This will help you to cancel out certain terms in order to simplify the equation.

3) Take small steps each time.  A proof is about working through a problem slowly so that it is easy to spot what has been done in each step. Do not take big leaps in your work such as multiplying out brackets and collecting like terms all at once. Remember that the person marking your paper needs to see your working, so it is good to work in small stages.

4) Go back and check your work.  Once you have finished your proof you can go back and check each individual stage. One of the good things about carrying out a proof is that you will know if a mistake has been made in your arithmetic because you will not be able to get to the final solution. If this happens, go back and check your working throughout.

Harder proofs

When working through a proof that is more difficult it can be quite tricky. Sometimes we may have to carry out a lot of different steps or even prove something using another piece of knowledge. For example, it might be that we are asked to prove that an expression will always be even or that it will always be positive.

In the above equation we have worked through to get an answer that is completely multiplied by 4. This must therefore be even as any number (whether even or odd) will be even when multiplied by 4.

In this example we have had to use our knowledge that anything multiplied by 4 must be even. This information was not included in the question but is something that we know from previous lessons. Some examples of information that you may need to know in order to solve more difficult proofs are:

Any number that is multiplied by an even number must be even

A number multiplied by an even number and then added to an odd number will be odd

Any number multiplied by a number will give an answer that is divisible by the same number (e.g. 3 n  must be divisible by 3)

Any number that is squared must be positive

Above we have come to an answer that is multiplied by 3. This means that the answer has to be divisible by 3 also.

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The Real Differences Between Thesis and Hypothesis (With table)

A thesis and a hypothesis are two very different things, but they are often confused with one another. In this blog post, we will explain the differences between these two terms, and help you understand when to use which one in a research project.

As a whole, the main difference between a thesis and a hypothesis is that a thesis is an assertion that can be proven or disproven, while a hypothesis is a statement that can be tested by scientific research. 

We probably need to expand a bit on this topic to make things clearer for you, let’s start with definitions and examples.

Definitions

As always, let’s start with the definition of each term before going further.

A thesis is a statement or theory that is put forward as a premise to be maintained or proved. A thesis statement is usually one sentence, and it states your position on the topic at hand.

A hypothesis is a statement that can be tested by scientific research. A hypothesis is usually based on observations, and it seeks to explain how these observations fit together.

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The best way to understand the slight difference between those terms, is to give you an example for each of them.

If you are writing a paper about the effects of climate change on the environment, your thesis might be “Climate change is causing irreparable damage to our planet, and we must take action to prevent further damage”.

If you observe that the leaves on a tree are turning yellow, your hypothesis might be “The tree is sick”. It’s the starting point of experimental research: what can you do then to prove if your hypothesis is right or wrong?

If your hypothesis is correct, then further research should be able to confirm it. However, if your hypothesis is incorrect, research will disprove it. Either way, a hypothesis is an important part of the scientific process.

Taking a look at the etymology of words can help you to remember which one to use is each case.

The word “thesis” comes from the Greek θέσις, meaning “something put forth”, and refers to an intellectual proposition.

The word “hypothesis” comes from the Greek words “hupo,” meaning “under”, and “thesis” that we just explained.

This reflects the fact that a hypothesis is an educated guess, based on observations.

Argumentation vs idea

Hypothesis are generally base on simple observation, while thesis imply that more work has been done on the topic.

A thesis is usually the result of extensive research and contemplation, and seeks to prove a point or theory.

A hypothesis is only a statement that need to be tested by observation or experimentation.

5 mains differences between thesis and hypothesis

Thesis and hypothesis are different in several ways, here are the 5 keys differences between those terms:

• A thesis is a statement that can be argued, while a hypothesis cannot be argued.
• A thesis is usually longer than a hypothesis.
• A thesis is more detailed than a hypothesis.
• A thesis is based on research, while a hypothesis may or may not be based on research.
• A thesis must be proven, while a hypothesis need not be proven.

So, in short, a thesis is an argument, while a hypothesis is a prediction. A thesis is more detailed and longer than a hypothesis, and it is based on research. Finally, a thesis must be proven, while a hypothesis does not need to be proven.

Is there a difference between a thesis and a claim?

Yes, there is a difference between a thesis and a claim. A thesis statement is usually one sentence that states your main argument, while a claim is a more general statement that can be supported by evidence.

Is a hypothesis a prediction?

No, a hypothesis is not a prediction. A prediction is a statement about what you think will happen in the future, whereas a hypothesis is a statement about what you think is causing a particular phenomenon.

What’s the difference between thesis and dissertation?

A thesis is usually shorter and more focused than a dissertation, and it is typically achieved in order to earn a bachelor’s degree. A dissertation is usually longer and more comprehensive, and it is typically completed in order to earn a master’s or doctorate degree.

What is a good thesis statement?

A good thesis statement is specific, debatable, and supports the main point of the paper. It should be clear what the researcher position is, and what evidence they will use to support it.

Thanks for reading! I hope this post helped clear up the differences between thesis and hypothesis. Like that kind of comparison? These other articles might be interesting for you:

• What is the Difference between Mandate and Law?
• The 6 Differences Between Space And Universe
• What’s the Difference Between Cosmology and Astrology?

I am very curious and I love to learn about all types of subjects. Thanks to my experience on the web, I share my discoveries with you on this site :)

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What Do You Do if Your Hypothesis Is Wrong?

Brenda priddy, 27 jun 2018.

A hypothesis is an idea that a scientist creates as the basis for an experiment. Typically, the hypothesis is based on previous findings, such as how certain chemicals react. The science experiment is designed to disprove or support the initial hypothesis. When the findings do not align with the hypothesis, the experiment is not a failure. When the results do not agree with the hypothesis, record the information just as if it did support the original hypothesis.

Explore this article

• Record Actual Results
• Explain What Was Wrong
• Additional Information
• New Hypothesis

1 Record Actual Results

When a hypothesis is disproven, that does not indicate a failed experiment. Most science experiments are designed to support or disprove a hypothesis. Recording actual results can sometimes be a struggle, especially if you wanted your hypothesis to be true. However, it's important to resist the temptation to record false results. The whole point of doing an experiment is to determine if something is true or not. In that sense, if your hypothesis is wrong, it doesn't necessarily mean that you're wrong. What matters is how you write-up your report. The results -- even if they're different from your hypothesis -- will demonstrate what you learned and how you might change the experiment next time.

2 Explain What Was Wrong

Make a list of everything that was wrong with the hypothesis. Make a second list with the any information that was correct in the original hypothesis. Write a short paragraph about each area where the hypothesis was correct or incorrect for a thorough explanation. Use photos, if possible, to illustrate the areas in which the hypothesis was incorrect.

3 Additional Information

Write down the information that was discovered from the experiment. Record the actual results and how they differed from the original hypothesis. Include notes for future experiments on the same topic that can help explore the idea further. Write down areas that need expansion for future experiments so that the results are more accurate, such as additional focus groups or a longer test period.

4 New Hypothesis

Create a new hypothesis for the same experiment. Although you disproved the initial hypothesis, you did not prove that something else will always happen. There is always room for more testing before something is completely proven. Write down the new hypothesis for future experiments. Present the findings together in a paper, at class or at a science fair.

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About the Author

Brenda Priddy has more than 10 years of crafting and design experience, as well as more than six years of professional writing experience. Her work appears in online publications such as Donna Rae at Home, Five Minutes for Going Green and Daily Mayo. Priddy also writes for Archstone Business Solutions and holds an Associate of Arts in English from McLennan Community College.

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