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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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• 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 .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

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

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

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

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

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

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

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

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Educational resources and simple solutions for your research journey

What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

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Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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

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

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

There are 5 main steps in hypothesis testing:

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Methodology

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

Research bias

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

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

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

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

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

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The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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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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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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Writing a Strong Hypothesis Statement

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

What is a hypothesis statement?

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

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

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

What is a hypothesis?

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

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

A strong hypothesis statement should be:

• A prediction of the relationship between two or more variables

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

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

Formulating your hypothesis

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

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

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

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

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

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

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

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

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

Refining your hypothesis

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

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

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

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

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

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

What makes a hypothesis weak?

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

Bad hypothesis 1: Diabetes is caused by witchcraft .

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

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

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

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

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

The null hypothesis and the alternative hypothesis

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

What is the null hypothesis?

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

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

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

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

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

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

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

Step-by-step sample hypothesis statements

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

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

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Start with a clear thesis question

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

Create a null hypothesis

Formulate an alternative hypothesis using the variables you have identified

Make sure your hypothesis clearly posits a relationship between variables

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

What makes a hypothesis strong? +

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

What makes a hypothesis weak? +

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

What is the null hypothesis? +

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

• Peterborough

Understanding Hypotheses and Predictions

Hypotheses and predictions are different components of the scientific method. The scientific method is a systematic process that helps minimize bias in research and begins by developing good research questions.

Research Questions

Descriptive research questions are based on observations made in previous research or in passing. This type of research question often quantifies these observations. For example, while out bird watching, you notice that a certain species of sparrow made all its nests with the same material: grasses. A descriptive research question would be “On average, how much grass is used to build sparrow nests?”

Descriptive research questions lead to causal questions. This type of research question seeks to understand why we observe certain trends or patterns. If we return to our observation about sparrow nests, a causal question would be “Why are the nests of sparrows made with grasses rather than twigs?”

In simple terms, a hypothesis is the answer to your causal question. A hypothesis should be based on a strong rationale that is usually supported by background research. From the question about sparrow nests, you might hypothesize, “Sparrows use grasses in their nests rather than twigs because grasses are the more abundant material in their habitat.” This abundance hypothesis might be supported by your prior knowledge about the availability of nest building materials (i.e. grasses are more abundant than twigs).

On the other hand, a prediction is the outcome you would observe if your hypothesis were correct. Predictions are often written in the form of “if, and, then” statements, as in, “if my hypothesis is true, and I were to do this test, then this is what I will observe.” Following our sparrow example, you could predict that, “If sparrows use grass because it is more abundant, and I compare areas that have more twigs than grasses available, then, in those areas, nests should be made out of twigs.” A more refined prediction might alter the wording so as not to repeat the hypothesis verbatim: “If sparrows choose nesting materials based on their abundance, then when twigs are more abundant, sparrows will use those in their nests.”

As you can see, the terms hypothesis and prediction are different and distinct even though, sometimes, they are incorrectly used interchangeably.

Let us take a look at another example:

Causal Question:  Why are there fewer asparagus beetles when asparagus is grown next to marigolds?

Hypothesis: Marigolds deter asparagus beetles.

Prediction: If marigolds deter asparagus beetles, and we grow asparagus next to marigolds, then we should find fewer asparagus beetles when asparagus plants are planted with marigolds.

A final note

It is exciting when the outcome of your study or experiment supports your hypothesis. However, it can be equally exciting if this does not happen. There are many reasons why you can have an unexpected result, and you need to think why this occurred. Maybe you had a potential problem with your methods, but on the flip side, maybe you have just discovered a new line of evidence that can be used to develop another experiment or study.

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scientific hypothesis

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• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

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Biology archive

Course: biology archive   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation., 2. ask a question., 3. propose a hypothesis., 4. make predictions., 5. test the predictions..

• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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• Hypothesis and Prediction

How do scientists come up with new hypotheses or predictions? They follow a step-by-step process known as the scientific method. This method turns a spark of curiosity into an established theory through research, planning and experimentation.

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Observation: scientists research something that they don’t understand. Once they've compiled their research, they write a simple question about the topic.

Hypothesis: scientists write an answer to their casual questions based on their research.

Prediction: scientists write down the outcome that they expect if their hypothesis is correct

Experiment: scientists gather evidence to see if their prediction is correct

Conclusion: this is the answer that the experiment provides. Does the evidence support the hypothesis?

Understanding the scientific method will help you to create, carry out and analyse your own testing and experiments.

Observation

The first step in the scientific method process is to observe something you wish to understand , learn from , or ask a question you would an answer to. This can be something general or as specific as you like.

Once you've decided on a topic, you will need to research it thoroughly using existing information. You can collect data from books, academic journals, textbooks, the internet and your own experiences. You could even carry out an informal experiment of your own!

Suppose you want to know the factors that affect the rate of a chemical reaction. After some research, you've discovered that temperature influences the rate of chemical reactions.

Your simple question could be : 'How does temperature affect the rate of reaction?'

What is the Definition of a Hypothesis?

After researching your topic using existing data and knowledge, you will write a hypothesis. This statement should help to answer your simple question.

A hypothesis is an explanation that leads to a testable prediction. In other words, it is a possible answer to the simple question posed during the observation step that can also be tested.

Your hypothesis should be based on a robust scientific rationale supported by the background research conducted in the first step using the scientific method.

Is a theory the same as a hypothesis?

What differentiates a theory from a hypothesis is that a theory tends to address a broader question supported by a vast amount of research and data. A hypothesis (as mentioned above) is a potential explanation for a much smaller and more specific question.

If experiments repeatedly support a hypothesis, that hypothesis can become a theory. However, theories can never become indisputable facts. Evidence supports, not proves, theories.

Scientists don't claim that their findings are correct. Instead, they state that their evidence supports their hypothesis.

Evolution and the Big Bang are widely accepted theories but can never be truly proven.

An Example of a Hypothesis in Science

During the observation stage, you discovered that temperature could affect the rate of a chemical reaction. Further research determined that the rate of reaction is faster at higher temperatures. This is because molecules require energy to collide and react with each other. The more energy there is (i.e., the higher the temperature), molecules will collide and react more often .

A good hypothesis could be:

‘Higher temperatures increase the rate of reaction because the particles have more energy to collide and react.’

This hypothesis makes for a possible explanation that we would be able to test to either prove it correct or not.

What is the Definition of a Prediction?

Predictions assume that your hypothesis is true.

A prediction is an outcome that is expected if the hypothesis is true.

Prediction statements typically use the words ‘if’ or ‘then’.

When putting a prediction together, it should point towards a relationship between an independent and dependent variable. An independent variable stands alone and isn't affected by anything else, whereas, a dependent variable can change due to the independent variable.

An Example of Prediction in Science

As a continuation of the example we are using in this article. A good prediction could be:

' If temperatures are increased, then the rate of reaction will increase.'

Note how if and then are used to articulate the prediction.

The independent variable would be the temperature . Therefore the dependent variable is the rate of reaction - this is the outcome we are interested in, and it depends on the first part of the prediction (the independent variable).

The Relationship and Difference Between Hypothesis and Prediction

Hypothesis and prediction are two different things, but they are frequently confused.

Both are statements assumed to be true, based on existing theories and evidence. However, there are a couple of key differences to remember:

A hypothesis is a general statement of how you think the phenomenon works.

Meanwhile, your prediction shows how you will test your hypothesis.

The hypothesis should always be written before the prediction.

Remember that the prediction should prove the hypothesis to be correct.

Gathering Evidence to Test the Prediction

The purpose of an experiment is to gather evidence to test your prediction. Gather your apparatus, measuring equipment and a pen to keep track of your results!

When magnesium reacts with water, it forms magnesium hydroxide, Mg(OH) 2 . This compound is slightly alkaline . If you add an indicator solution to the water, it will change colour when magnesium hydroxide has been produced and the reaction is complete.

To test the reaction rate at different temperatures, heat beakers of water to the desired temperature, then add the indicator solution and the magnesium. Use a timer to track how long it takes for the water to change colour for each water temperature. The less time it takes for the water to change colour, the faster the rate of reaction.

Make sure to keep your control variables the same. The only thing you want to change is the temperature of the water.

Accepting or Rejecting the Hypothesis

The conclusion shows the results of the experiment - have you found evidence to support your prediction?

If your results match your prediction, you accept the hypothesis.

If your results don’t match your prediction, you reject the hypothesis.

You can’t prove your hypothesis, but you can say that your results support the hypothesis that you’ve made. If your evidence backs up your prediction, you are one step closer to figuring out if your hypothesis is true.

If the results of your experiment don't match your prediction or hypothesis, you shouldn't change them. Instead, reject your hypothesis and consider why your results didn't fit. Did you make any errors during your experiment? Did you make sure all the control variables were kept the same?

The less time it takes for the magnesium to react, the faster the rate of reaction.

Will you accept or reject the original hypothesis?

Remember that a hypothesis is an explanation for why something happens. The hypothesis is used to make the prediction - the outcome you would get if your hypothesis is true.

Hypothesis and Prediction - Key takeaways

• The scientific method is a step-by-step process: observation, hypothesis, prediction, experiment and conclusion.
• The first stage, observation, is researching your chosen topic.
• Next, you will write a hypothesis: an explanation that leads to a testable prediction.
• Then you will write a prediction: the expected outcome if your hypothesis is true.
• The experiment gathers evidence to test your prediction.
• If your results match your prediction, you can accept your hypothesis. Remember that acceptance doesn't mean proof.

1. CGP, GCSE AQA Combined Science Revision Guide , 2021

2. Jessie A. Key, Factors that Affect the Rate of Reactions, Introductory Chemistry - 1st Canadian Edition, 2014

3. Neil Campbell, Biology: A Global Approach Eleventh Edition , 2018

4. Paul Strode, The Global Epidemic of Confusing Hypotheses with Predictions Fixing an International Problem, Fairview High School, 2011

5. Science Made Simple, The Scientific Method, 2019

6. Trent University, Understanding Hypotheses and Predictions , 2022

7. University of Massachusetts, Effect of Temperature on the Reactivity of Magnesium in Water , 2011

Flashcards in Hypothesis and Prediction 14

What is observation in the scientific process?

Observation is the research stage.

What is a prediction?

What words are typically used in predictions?

What comes first?

What is the purpose of an experiment?

The purpose of an experiment is to gather evidence to support your prediction.

What is the chemical formula for magnesium hydroxide?

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Frequently Asked Questions about Hypothesis and Prediction

What is the relationship between a hypothesis and a prediction?

A hypothesis is an explanation of why something happens. This is used to make a testable prediction.

What is an example of a hypothesis and prediction?

Hypothesis: 'Higher temperatures increase the rate of reaction because the particles have more energy to collide and react.'

Prediction: 'If temperatures are increased, then the rate of reaction will increase.'

What is the difference between hypothesis, prediction and inference?

A hypothesis is an explanation, a prediction is the expected outcome, and an inference is a conclusion reached.

How can you write a prediction in science?

Predictions are statements that assume your hypothesis is true. Use the words 'if' and 'when'. For example, 'if temperatures are increased, then the rate of reaction will increase.'

What comes first, hypothesis or prediction?

The hypothesis comes before the prediction.

Test your knowledge with multiple choice flashcards

When do you accept the hypothesis?

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Hypothesis vs Prediction: When To Use Each One In Writing

Are you confused about the difference between a hypothesis and a prediction? You’re not alone. Many people use these terms interchangeably, but they actually have distinct meanings in the scientific world.

A hypothesis is a proposed explanation for a phenomenon. It is an educated guess based on observations and prior knowledge. A hypothesis can be either proven or disproven through experimentation and analysis of data. On the other hand, a prediction is a statement about what will happen in the future. It is based on current knowledge and can be tested through observation and data analysis.

Throughout this article, we will explore the differences between hypotheses and predictions in more detail. By the end, you’ll have a better understanding of how these concepts are used in scientific research.

Define Hypothesis

A hypothesis is a proposed explanation for a phenomenon or observation. It is an educated guess that is based on existing knowledge and is used to guide further investigation. In scientific research, a hypothesis is formulated as a statement that can be tested through observation or experimentation. A hypothesis can be either null or alternative. A null hypothesis is a statement that suggests there is no relationship between variables, while an alternative hypothesis is a statement that suggests there is a relationship between variables.

Define Prediction

A prediction is a statement about what is expected to happen in the future based on existing knowledge. It is an educated guess that is made based on observations and data analysis. Predictions can be made in various fields, including science, economics, and sports. In scientific research, predictions are made based on hypotheses. If a hypothesis is supported by evidence, then predictions can be made based on the expected outcomes of further experiments or observations. Predictions can be either qualitative or quantitative, depending on the nature of the phenomenon being studied.

How To Properly Use The Words In A Sentence

Using the correct terminology in scientific writing is crucial. In this section, we will discuss how to properly use the words “hypothesis” and “prediction” in a sentence.

How To Use Hypothesis In A Sentence

A hypothesis is a proposed explanation for a phenomenon or observation. It is an educated guess based on prior knowledge and research. Here are some examples of how to use “hypothesis” in a sentence:

• Our hypothesis is that increased exposure to sunlight will lead to an increase in vitamin D levels.
• The researchers developed a hypothesis to explain the results of their experiment.
• Before conducting the study, the scientists formulated a hypothesis based on previous research.

Note that a hypothesis is not a proven fact, but rather a starting point for further investigation. It should be testable and falsifiable.

How To Use Prediction In A Sentence

A prediction is a statement about what will happen in the future based on past experience or evidence. It is an estimate or forecast of what might occur. Here are some examples of how to use “prediction” in a sentence:

• The weather forecast predicted heavy rain for the next three days.
• Based on the data collected, the researchers predicted that the new treatment would be effective.
• His prediction that the stock market would crash turned out to be accurate.

Unlike a hypothesis, a prediction is a statement of what is expected to happen. It can be tested and verified or proven false.

More Examples Of Hypothesis & Prediction Used In Sentences

In this section, we will provide additional examples of how hypothesis and prediction can be used in sentences. By understanding how these terms are used in context, you can better differentiate between the two and apply them appropriately in your own writing.

Examples Of Using Hypothesis In A Sentence

• My hypothesis is that the new marketing campaign will increase sales by 20%.
• The hypothesis that the earth is flat has been disproven by scientific evidence.
• Based on my hypothesis, I predict that the stock market will crash next month.
• She formulated a hypothesis that the plant growth was affected by the amount of sunlight it received.
• My hypothesis is that the blue shirt will sell better than the red one.
• The hypothesis that the vaccine is causing side effects has not been supported by clinical trials.
• Scientists are testing the hypothesis that a lack of sleep can lead to decreased cognitive function.
• My hypothesis is that the new recipe will result in a more flavorful dish.
• The hypothesis that climate change is caused by human activity is widely accepted in the scientific community.
• Based on my hypothesis, I believe that increasing the price of the product will lead to a decrease in demand.

Examples Of Using Prediction In A Sentence

• My prediction is that the team will win the championship this year.
• The weather forecast predicts rain for the next three days.
• Based on my prediction, I expect the company’s profits to double next quarter.
• The psychic made a prediction that the couple would have a baby boy.
• My prediction is that the price of oil will continue to rise in the coming months.
• The economist’s prediction of a recession was accurate.
• I predict that the new restaurant will become very popular due to its unique menu.
• The weatherman’s prediction of a snowstorm caused many people to stock up on supplies.
• Based on my prediction, I think that the political party will win the majority of seats in the upcoming election.
• The astrologer’s prediction of a major life change was met with skepticism by some.

Common Mistakes To Avoid

While hypothesis and prediction are related concepts, they are not interchangeable. Unfortunately, many people make the mistake of using them interchangeably, leading to confusion and incorrect conclusions. Here are some common mistakes to avoid:

Mistake #1: Using Hypothesis And Prediction Interchangeably

One of the most common mistakes people make is using hypothesis and prediction interchangeably. A hypothesis is an educated guess or explanation for a phenomenon, while a prediction is a statement about what will happen in the future based on past experience or knowledge.

For example, if you were conducting an experiment to test the effect of fertilizer on plant growth, your hypothesis might be that the plants will grow taller with the use of fertilizer. Your prediction, on the other hand, would be a specific statement about what you expect to happen, such as “the plants will grow 10% taller with the use of fertilizer.”

Using hypothesis and prediction interchangeably can lead to confusion and incorrect conclusions. It’s important to understand the difference between the two and use them appropriately.

Mistake #2: Failing To Test Hypotheses

Another common mistake is failing to test hypotheses. A hypothesis is only useful if it can be tested and either proven or disproven. If you have a hypothesis but don’t test it, you won’t know if it’s accurate or not.

For example, if you hypothesize that a certain medication will reduce symptoms of a particular disease, you need to conduct a study to see if the medication actually works. If you don’t test your hypothesis, you won’t know if it’s accurate or not.

Mistake #3: Making Predictions Without Evidence

Finally, another common mistake is making predictions without evidence. A prediction should be based on past experience or knowledge, not just a guess or hunch.

For example, if you predict that a certain stock will go up in value, you should have evidence to support that prediction, such as past performance or market trends. If you make a prediction without evidence, you’re just guessing, and your prediction is unlikely to be accurate.

Tips To Avoid These Mistakes

To avoid these common mistakes, it’s important to:

• Understand the difference between hypothesis and prediction
• Test your hypotheses to see if they’re accurate
• Base your predictions on evidence, not just guesswork

By following these tips, you can ensure that you’re using hypothesis and prediction correctly and making accurate conclusions based on your research and analysis.

Context Matters

When it comes to scientific research, choosing between a hypothesis and a prediction can depend on the context in which they are used. While both are important tools used to make educated guesses about the outcome of an experiment or study, they are not interchangeable. The choice between hypothesis and prediction can have a significant impact on the design and interpretation of research.

Examples Of Different Contexts

Let’s consider a few different contexts and how the choice between hypothesis and prediction might change:

Context 1: Exploratory Research

In exploratory research, the goal is to gather information and generate new ideas or hypotheses. In this context, a hypothesis may be too narrow and limit the scope of the research. Instead, a prediction may be more appropriate as it allows for more flexibility and openness to unexpected outcomes. For example, a researcher studying the effects of a new drug on a specific disease may start with a prediction that the drug will have some effect, but without a specific hypothesis about the mechanism of action.

Context 2: Confirmatory Research

In confirmatory research, the goal is to test a specific hypothesis or theory. In this context, a hypothesis is necessary to guide the research and ensure that the study is focused and relevant. A prediction may be too vague and not provide enough direction for the research. For example, a researcher testing the hypothesis that a particular gene is responsible for a certain disease would need to design a study that specifically tests that hypothesis, rather than just making a prediction about the relationship between the gene and the disease.

Context 3: Predictive Modeling

In predictive modeling, the goal is to develop a model that can accurately predict future outcomes based on past data. In this context, both hypotheses and predictions may be used, but in different ways. A hypothesis may be used to develop a theory or model that explains the relationship between variables, while a prediction may be used to test the accuracy of the model and make predictions about future outcomes. For example, a researcher developing a model to predict the likelihood of a patient developing a certain disease may start with a hypothesis about the risk factors involved, but use predictions to test the accuracy of the model and make predictions about future patients.

As we can see, the choice between hypothesis and prediction can depend on the context in which they are used. While both are important tools in scientific research, they serve different purposes and can have different implications for the design and interpretation of research. Understanding the appropriate use of each can help researchers make informed decisions and conduct more effective research.

Exceptions To The Rules

While the rules for using hypothesis and prediction are generally straightforward, there are some exceptions to keep in mind.

1. When The Context Requires A Specific Approach

In some cases, the context of the situation may require a specific approach that deviates from the typical use of hypothesis and prediction. For example, in legal proceedings, the term “hypothesis” may refer to a proposed explanation for a crime, while “prediction” may refer to a statement about the likelihood of a particular outcome in a trial.

2. When The Distinction Is Not Relevant

There may be situations where the distinction between hypothesis and prediction is not relevant or necessary. For instance, in casual conversation, people may use the terms interchangeably without any significant impact on the conversation’s meaning.

3. When The Terms Are Used In A Non-scientific Context

Outside of scientific contexts, the terms hypothesis and prediction may have different meanings or connotations. For example, in business, a hypothesis may refer to a proposed solution to a problem, while a prediction may refer to a projection of future trends.

4. When The Terms Are Used In Different Scientific Disciplines

The use of hypothesis and prediction may vary across scientific disciplines. For example, in psychology, a hypothesis may refer to a proposed explanation for a phenomenon, while a prediction may refer to a statement about the expected outcome of an experiment.

It is essential to keep in mind these exceptions to ensure that the use of hypothesis and prediction is appropriate for the given context.

Practice Exercises

Now that we have a good understanding of the differences between hypotheses and predictions, it’s time to put that knowledge to the test. Below are some practice exercises to help you improve your understanding and use of these concepts in sentences. Each exercise comes with an answer key or explanation to help you check your work and learn from any mistakes you might make.

For each of the following statements, determine whether it is a hypothesis or a prediction:

Answer Key:

• If I eat less sugar, I will lose weight. – Prediction
• People who exercise regularly are less likely to develop heart disease. – Hypothesis
• Adding fertilizer to plants will make them grow taller. – Prediction
• Children who read at least 20 minutes a day have higher reading comprehension scores. – Hypothesis

Write a hypothesis or prediction for each of the following scenarios:

• A researcher is studying the effects of caffeine on reaction time.
• A farmer wants to increase the yield of his crops.
• A teacher is interested in whether or not students who take notes during lectures perform better on exams.
• Hypothesis: Consuming caffeine will improve reaction time.
• Prediction: Adding more fertilizer to the soil will increase crop yield.
• Hypothesis: Students who take notes during lectures will perform better on exams than those who do not.

By practicing these exercises, you can improve your ability to distinguish between hypotheses and predictions and use them effectively in your own writing and research.

In conclusion, understanding the difference between a hypothesis and a prediction is crucial for effective communication in both scientific and everyday settings. Here are some key takeaways from this article:

Hypotheses And Predictions Are Not Interchangeable Terms

While both involve making educated guesses about the outcome of a situation, hypotheses are broader and more open-ended, while predictions are specific and testable. It’s important to use the correct term in order to avoid confusion or miscommunication.

Hypotheses And Predictions Are Important In Scientific Research

Scientists use hypotheses and predictions to guide their research and to make predictions about the outcome of experiments. By doing so, they can test their theories and gain a better understanding of the natural world.

Using The Correct Term Can Enhance Your Credibility

Whether you’re writing a scientific paper or simply discussing a topic with colleagues, using the correct terminology can enhance your credibility and demonstrate your knowledge of the subject matter.

If you’re interested in learning more about grammar and language use, there are many resources available online and in print. By continuing to improve your understanding of these concepts, you can become a more effective communicator and a more knowledgeable member of your field.

Shawn Manaher is the founder and CEO of The Content Authority. He’s one part content manager, one part writing ninja organizer, and two parts leader of top content creators. You don’t even want to know what he calls pancakes.

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• Published: 19 June 2024

Reward prediction error neurons implement an efficient code for reward

• Heiko H. Schütt   ORCID: orcid.org/0000-0002-2491-5710 1 , 2   na1 ,
• Dongjae Kim 1 , 3   na1 &
• Wei Ji Ma   ORCID: orcid.org/0000-0002-9835-9083 1  

Nature Neuroscience ( 2024 ) Cite this article

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We use efficient coding principles borrowed from sensory neuroscience to derive the optimal neural population to encode a reward distribution. We show that the responses of dopaminergic reward prediction error neurons in mouse and macaque are similar to those of the efficient code in the following ways: the neurons have a broad distribution of midpoints covering the reward distribution; neurons with higher thresholds have higher gains, more convex tuning functions and lower slopes; and their slope is higher when the reward distribution is narrower. Furthermore, we derive learning rules that converge to the efficient code. The learning rule for the position of the neuron on the reward axis closely resembles distributional reinforcement learning. Thus, reward prediction error neuron responses may be optimized to broadcast an efficient reward signal, forming a connection between efficient coding and reinforcement learning, two of the most successful theories in computational neuroscience.

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Acknowledgements

We thank H.-H. Li for valuable discussions. We received no specific funding for this work.

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These authors contributed equally: Heiko H. Schütt, Dongjae Kim.

Authors and Affiliations

Center for Neural Science and Department of Psychology, New York University, New York, NY, USA

Heiko H. Schütt, Dongjae Kim & Wei Ji Ma

Department of Behavioural and Cognitive Sciences, Université du Luxembourg, Esch-Belval, Luxembourg

Heiko H. Schütt

Department of AI-Based Convergence, Dankook University, Yongin, Republic of Korea

Dongjae Kim

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H.H.S. derived the efficient code. H.H.S. and D.K. analyzed the neural data. W.J.M. supervised the project. All authors wrote the manuscript.

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Extended data

Extended data fig. 1 comparing encoding populations for reward with 10 neurons and the same expected number of spikes..

A : Compared neuronal populations: single neuron : All neurons share the same response curve, optimized to maximize transferred information. equal spacing : neurons tile the space, not optimized. no gain : positions and slopes are optimized, but all neurons have equal gain. optimal α  = 1: fully optimized population as derived previously 18 with density proportional to the distribution. optimal α  = 0.673: Equally optimal distribution but with α fit to match the midpoint distribution for the optimal code and the experimental data. B : Fisher information as a function of reward for each of the populations. C : Expected logarithm of Fisher information under the reward distribution relative to the single-neuron case.

Extended Data Fig. 2 Illustration of the solution of the efficient coding problem, varying α (rows) and the reward distribution (columns).

The reward distributions are all log-normal distributions with their pdfs and parameters plotted at the top.

Extended Data Fig. 3 Efficient code for the variable-reward task 14 .

A : Tuning curves. For clarity, only 20 of 39 neurons are shown. B : Density of neurons as a function of midpoint. C : Gain as a function of midpoint.

Extended Data Fig. 4 Log-normal kernel density estimation of midpoints and threshold.

A : Midpoints. B : Thresholds. Measured neurons (black) and efficient code (cyan) are overlayed over the reward density (gray).

Extended Data Fig. 5 Efficient code for the variable-magnitude task 17 .

A-C : Efficient code for the uniform distribution. D-F : Efficient code for the normal distribution. A,D : Tuning curves. For clarity, only 13 of 40 neurons are shown. B,E : Density. C,F : Gain.

Extended Data Fig. 6 Evaluation of learning rules placing neurons’ midpoints at expectiles instead of quantiles.

Plotting conventions as in Fig. 4 . Each panel shows the converged population of 20 neurons after learning based on 20, 000 reward presentations. The inset illustrates the learning rule. A : Learning the position on the reward axis for the neurons to converge to the quantiles of the distribution. This learning rule is the distribution RL learning rule. B : Additionally learning the slope of the neurons to be proportional to the local density by increasing the slope when the reward falls within the dynamic range and decreasing otherwise. C : First method to set the gain: iterative adjustment to converge to a fixed average firing rate. D : Second method to set the gain: use a fixed gain per neuron based on the quantile it will eventually converge to. E : The efficient tuning curve for a single neuron. F : The analytically derived optimal solution. G : Comparison of information transfer across the different populations with the same number of neurons and expected firing rate.

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Schütt, H.H., Kim, D. & Ma, W.J. Reward prediction error neurons implement an efficient code for reward. Nat Neurosci (2024). https://doi.org/10.1038/s41593-024-01671-x

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Reliability prediction for new prefabricated track structures based on the fuzzy failure modes, effects, and criticality analysis method.

1. Introduction

2. the fuzzy fmeca method, 2.1. construct the factor set, 2.2. constructing the evaluation set, 2.3. constructing the fuzzy evaluation matrix, 2.4. constructing the weight vector, 2.4.1. calculate the entropy value, 2.4.2. construct the weight vector, 2.5. determine the criticality, 3. fuzzy fmeca for new prefabricated track structures, 3.1. composition of track structures, 3.2. fmeca of track structures, 3.3. fuzzy fmeca of track structures, 3.3.1. construction of factor set, 3.3.2. construction of evaluation set, 3.4. constructing the weight vector, 3.5. determining criticality, 4. reliability prediction for new prefabricated track structures, 4.1. reliability prediction model based on a similar product method, 4.2. reliability prediction, 5. discussion, 6. conclusions.

• The overall criticality value of the two track structures was calculated using the fuzzy FMECA method. The overall criticality value of the CRTS II slab track structure was 34.7464, while the overall criticality value of the modular assembled track structure with built-in position retention was 33.5803, which indicates that the latter has lower criticality.
• A reliability prediction model based on the similar product method was established, and the reliability of the modular assembled track structure with built-in position retention was quantitatively estimated. According to the reliability prediction model, the reliability of the modular assembled track structure with built-in position retention is 0.9994, which indicates a high level of reliability.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Huang, C.; Wu, J.; Shan, Z.; Wang, Q.; Yu, Z. Reliability Prediction for New Prefabricated Track Structures Based on the Fuzzy Failure Modes, Effects, and Criticality Analysis Method. Appl. Sci. 2024 , 14 , 5338. https://doi.org/10.3390/app14125338

Huang C, Wu J, Shan Z, Wang Q, Yu Z. Reliability Prediction for New Prefabricated Track Structures Based on the Fuzzy Failure Modes, Effects, and Criticality Analysis Method. Applied Sciences . 2024; 14(12):5338. https://doi.org/10.3390/app14125338

Huang, Chao, Jun Wu, Zhi Shan, Qing’e Wang, and Zhiwu Yu. 2024. "Reliability Prediction for New Prefabricated Track Structures Based on the Fuzzy Failure Modes, Effects, and Criticality Analysis Method" Applied Sciences 14, no. 12: 5338. https://doi.org/10.3390/app14125338

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Jimmy Butler's Future With The Miami Heat Could Reportedly Be In Jeopardy

Ben stinar | jun 21, 2024.

UPDATE : Howard Beck noted that his statement is just a prediction and not a report.

Predictions are not "reports." Predictions are not "reports." Predictions are not "reports." Predictions are not "reports." Predictions are not "reports." Predictions are not "reports." Predictions are not "reports." Thank you for coming to my Ted Talk. — Howard Beck (@HowardBeck) June 21, 2024

Jimmy Butler is still among the best players in the NBA at 34.

He is coming off a season where he averaged 20.8 points, 5.3 rebounds, 5.0 assists and 1.3 steals per contest while shooting 49.9% from the field and 41.4% from the three-point range in 60 games.

However, he got injured at the end of the season and did not play in the 2024 NBA playoffs.

The six-time NBA All-Star has been a hot topic in the early days of the offseason.

He has at least one more year on his contract before having to decide on a $52.4 million player option for the 2025-26 season.

Talks of an extension will be a major storyline.

Recently, Howard Beck of The Ringer made an intriguing prediction in an article the outlet did on the upcoming offseason.

Beck (via The Ringer) : "Jimmy Butler will be traded. Rival executives have been buzzing about it for months, for all the obvious reasons."

Which stars will change teams this offseason? Which general manager faces the most perplexing summer? Our staff weighs in on those questions and more at the outset of a busy NBA offseason. https://t.co/3qdgsb08Vq — The Ringer (@ringer) June 21, 2024

Butler has been with Miami for five seasons.

In that span, they have made the NBA Finals twice and reached the Eastern Confernece finals three times.

He has been the team's most important player (by far), so the Heat have a very tough decision to make.

Butler was initially the 30th pick in the 2011 NBA Draft and he has played 13 seasons in the league.

In addition to the Heat, he has also spent time with the Chicago Bulls, Minnesota Timberwolves and Philadelphia 76ers.

Last season, the Heat lost to the Boston Celtics in the first round of the NBA playoffs.

Ben Stinar is the NBA reporter for Fastbreak on FanNation.

Follow @BenStinar

Biden announces new policy shielding undocumented spouses of U.S. citizens from deportation

President Joe Biden is taking executive action to protect undocumented spouses of American citizens — a move that would shield about 500,000 immigrants from deportation.

The White House announced the election-year policy Tuesday, framing it as “new action to keep families together.” NBC News reported last week that action protecting the spouses was likely to be announced soon, after urging from immigration advocates and Democratic lawmakers and as the president courts Latino voters in crucial battleground states.

The new policy would allow noncitizens who have been in the country for at least 10 years and are married to a U.S. citizen, and their children, to apply for permanent residence without leaving the country.

During a ceremony at the White House, Biden called the steps a "commonsense fix" to a system that is "cumbersome, risky and separates families."

He said the order would go into effect this summer and stressed that it would not benefit people who recently came into the country. Instead, it would help people who are "paying taxes and contributing to our country" and their family members.

“This is the biggest thing since DACA,” said a source familiar with the matter, an immigration advocate.

The Deferred Action for Childhood Arrivals program, announced by then-President Barack Obama in 2012, allowed immigrants who illegally came to the U.S. as children to stay in the country.

Foreshadowing the likely battles to come over the policy, the White House was keen to stress that it has been tough on unlawful border crossings and has worked to dismantle people-smuggling networks.

The president "believes that securing the border is essential," it said in a news release Tuesday outlining the new action.

"He also believes in expanding lawful pathways and keeping families together, and that immigrants who have been in the United States for decades, paying taxes and contributing to their communities, are part of the social fabric of our country," the statement said.

The statement added that the spouses eligible to apply for this have been in the U.S. for 23 years on average.

The program would also make it easier for some undocumented immigrants to get a green card and a path to U.S. citizenship.

Sources also say that the undocumented spouses would be allowed to obtain work permits on a case-by-case basis.

The action includes plans to allow DACA recipients who earned degrees in higher education and are seeking a job in that same field to more quickly receive work visas.

Republicans, including former President Donald Trump , blasted the executive action.

Speaking at a campaign rally in Wisconsin, Trump said that if he's elected in November, Biden’s new immigration policy would be immediately “ripped up and thrown out.”

"It's been a nonstop catastrophe, but one of crooked Joe's most destructive moves yet is the lawless executive action he's taken today," Trump said. "Under this program, a deluge of illegals will be given immediate green cards and put on the fast track to rapid citizenship so they can vote."

The presumptive Republican nominee for president, who has made immigration and border issues a cornerstone of his campaign, said "millions" of immigrants would benefit from the program, a figure that contrasts with a White House estimate that it would impact roughly 500,000 people who are spouses and 50,000 non-citizen children who are under 21 with a non-citizen parent who married an American before they were 18 who may also qualify.

House Speaker Mike Johnson, R-La., also ripped the administration's announcement, painting it as "granting amnesty to hundreds of thousands of illegal aliens" and saying it would "incentivize more illegal immigration and endanger Americans."

The new program is expected to be challenged in court.

Noting the likelihood of lawsuits, Senate Majority Whip Dick Durbin, D-Ill., who chairs the Senate Judiciary Committee, said in a statement that passing legislation would be "the only action that will fully allow these deserving individuals to put down roots, start families, further their education, and continue contributing to our society without fear of deportation." But he also acknowledged that getting a bill through Congress would be unlikely given Republican opposition to previous immigration overhauls.

Gabe Gutierrez is a senior White House correspondent for NBC News.

Julie Tsirkin is a correspondent covering Capitol Hill.

Julia Ainsley is the homeland security correspondent for NBC News and covers the Department of Homeland Security for the NBC News Investigative Unit.

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Blood test could predict Parkinson’s seven years before symptoms

18 June 2024

A team of researchers, led by scientists at UCL and University Medical Center Goettingen, Germany, have developed a simple blood test that uses artificial intelligence (AI) to predict Parkinson’s up to seven years before the onset of symptoms.

Parkinson’s disease is the world’s fastest growing neurodegenerative disorder and currently affects nearly 10 million people across the globe.

The condition is a progressive disorder that is caused by the death of nerve cells in the part of the brain called the substantia nigra, which controls movement. These nerve cells die or become impaired, losing the ability to produce an important chemical called dopamine, due to the build-up of a protein alpha-synuclein.

Currently, people with Parkinson’s are treated with dopamine replacement therapy after they have already developed symptoms, such as tremor, slowness of movement and gait, and memory problems. But researchers believe that early prediction and diagnosis would be valuable for finding treatments that could slow or stop Parkinson’s by protecting the dopamine producing brain cells.

Senior author, Professor Kevin Mills (UCL Great Ormond Street Institute of Child Health), said: “As new therapies become available to treat Parkinson’s, we need to diagnose patients before they have developed the symptoms. We cannot regrow our brain cells and therefore we need to protect those that we have.

“At present we are shutting the stable door after the horse has bolted and we need to start experimental treatments before patients develop symptoms.  Therefore, we set out to use state-of-the-art technology to find new and better biomarkers for Parkinson’s disease and develop them into a test that we can translate into any large NHS laboratory. With sufficient funding, we hope that this may be possible within two years.”

The research, published in Nature Communications , found that when a branch of AI called machine learning, analysed a panel of eight blood based biomarkers whose concentrations are altered in patients with Parkinson’s, it could provide a diagnosis with 100% accuracy.

The team then experimented to see whether the test could predict the likelihood that a person would go on to develop Parkinson’s.

They did this by analysing blood from 72 patients with Rapid Eye Movement Behaviour Disorder (iRBD). This disorder results in patients physically acting out their dreams without knowing it (having vivid or violent dreams). It is now known that about 75-80% of these people with iRBD will go on to develop a synucleinopathy (a type of brain disorder caused by the abnormal buildup of a protein called alpha-synuclein in brain cells) – including Parkinson’s.

When the machine learning tool analysed the blood of these patients it identified that 79% of the iRBD patients had the same profile as someone with Parkinson’s.

The patients were followed up over the course of ten years and the AI predictions have so far matched the clinical conversion rate – with the team correctly predicting 16 patients as going on to develop Parkinson’s and being able to do this up to seven years before the onset of any symptoms. The team are now continuing to follow up on those predicted to develop Parkinson’s, to further verify the accuracy of the test.

Co-first-author Dr Michael Bartl (University Medical Center Goettingen) who conducted the research from the clinical side alongside Dr Jenny Hällqvist (UCL Great Ormond Street Institute of Child Health), said: "By determining 8 proteins in the blood, we can identify potential Parkinson's patients several years in advance. This means that drug therapies could potentially be given at an earlier stage, which could possibly slow down disease progression or even prevent it from occurring.

“We have not only developed a test, but can diagnose the disease based on markers that are directly linked to processes such as inflammation and degradation of non-functional proteins. So these markers represent possible targets for new drug treatments.”

Co-author, Professor Kailash Bhatia (UCL Queen Square Institute of Neurology and National Hospital for Neurology & Neurosurgery) and his team are currently examining the test’s accuracy by analysing samples from those in the population who are at high risk of developing Parkinson’s, for example those with mutations in particular genes such as ‘LRRK2’ or ‘GBA’ that cause Gaucher disease.

The team are also hoping to secure funding to create a simpler blood spot test where a drop of blood can be spotted on a card and posted to the lab to investigate if it can predict Parkinson’s disease even earlier than the seven years before the onset of symptoms in this study. 

The research was funded by an EU Horizon 2020 grant, Parkinson’s UK, the  National Institute for Health and Care Research GOSH Biomedical Research Centre (NIHR GOSH BRC), and the Szeben-Peto Foundation.

Professor David Dexter, Director of Research at Parkinson’s UK, said: “This research, co-funded by Parkinson’s UK, represents a major step forward in the search for a definitive and patient friendly diagnostic test for Parkinson’s. Finding biological markers that can be identified and measured in the blood is much less invasive than a lumbar puncture, which is being used more and more in clinical research.

“With more work, it may be possible that this blood based test could distinguish between Parkinson’s and other conditions that have some early similarities, such as Multiple Systems Atrophy or Dementia with Lewy Bodies.

“The findings add to an exciting flurry of recent activity towards finding a simple way to test for and measure Parkinson’s.”

• Research in  Nature Communications
• Professor Kevin Mills' academic profile
• Professor Kailash Bhatia's academic profile
• UCL Great Ormond Street Institute of Child Health
• UCL Queen Square Institute of Neurology
• UCL Population Health Sciences
• UCL Brain Sciences
• Credit:  Jacob Wackerhausen  on iStock

Media contact 

Poppy tombs .

E: p.tombs [at] ucl.ac.uk

RNAfold: RNA tertiary structure prediction using variational autoencoder.

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Understanding the RNA tertiary structure is crucial for advancing therapeutic development and vaccine design. Previous methods, such as dynamic simulations, have been used to study RNA structure. However, these methods often struggle to capture the complex non-linear relationships in RNA sequences. Other approaches focused on simple models, resulting in lower performance. Indeed, one of the main challenges in predicting RNA tertiary structure is the lack of data, and using complicated algorithms is limited. In this paper, we introduce the Variational Autoencoder (VAE). In this paper, we introduce the Variational Autoencoder (VAE) for the problem of RNA folding and demonstrate that the Variational Autoencoder could yield promising results. Our model achieves a Root Mean Square Error (RMSE) of approximately 3.3 Angstrom in predicting nucleotide positions, significantly outperforming existing approaches.

Competing Interest Statement

The authors have declared no competing interest.

https://github.com/quantori/RNAfold

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Despite AI enthusiasm, Workforce Index reveals workers aren’t yet unlocking its benefits

By the team at Slack June 3rd, 2024

Quick take: Using AI tools at work is associated with a host of positive outcomes, from improved productivity to higher employee satisfaction. But executive urgency to incorporate AI is outpacing its use among employees. A new global survey of desk workers from the Workforce Lab at Slack, a Salesforce company, finds that two-thirds of workers have still not tried AI tools and 93% do not consider AI outputs completely trustworthy for work-related tasks.

Read on to learn about the top AI blockers for workers, the surprising AI gender gap emerging among Gen Z, and why we believe the AI hype cycle is just beginning.

Key findings

• Executive urgency to incorporate AI tools into business operations has increased 7x over the past six months and is now a top concern, above inflation or the broader economy.
• Among desk workers who use AI tools, 81% say it’s improving their productivity. And those who use AI show higher employee engagement and experience scores across the board, including 22% higher overall satisfaction.
• And yet more than two-thirds of desk workers have never used AI at work, and nearly 2 in 5 say their company has no AI usage guidelines.
• Just 7% of desk workers consider the outputs of AI completely trustworthy for work-related tasks , with 35% of desk workers saying AI results are only slightly or not at all trustworthy.
• There’s an AI gender gap emerging, and it’s largest among Gen Z. While young people are most likely to have experimented with AI tools, Gen Z men are 25% more likely to have tried AI tools compared with Gen Z women.
• Desk workers report spending a third of their day on average on tasks they consider low-value. But troublingly, instead of allocating the time saved by AI toward strategic or high-value activities like learning and skill building, employees are likely to spend 37% more of their time on routine administrative tasks.
• Even so, the AI hype cycle shows no signs of slowing. Seventy-three percent of desk workers say that AI hype is warranted and the technology “will have a big impact,” and those who have used AI tools are even more convinced.

See the data: June 2024 Workforce Index

In its latest survey of more than 10,000 desk workers around the globe, the Workforce Lab from Slack, a Salesforce company, finds that nearly all executives (96%) now feel an urgency to incorporate AI into business operations. The share of leaders aiming to do this “in the next 18 months” has grown 7x since September 2023, rising from 5% to 35% of all executives. And AI innovation now tops the list of executives ’ external concerns, above inflation or the broader economy.

At the same time, AI use among desk workers is up 23% since January and 60% since September. Thirty-two percent of desk workers have now experimented with AI tools and half of that group is using AI at work at least weekly.

Most AI users (81%) report that AI tools are improving their productivity. And notably, those using AI show higher scores across the board on all measures of employee engagement and experience, including:

• +13% Level of access to relevant people, files and resources
• +18% Work-life balance
• +23% Ability to manage stress
• +24% Overall satisfaction with work
• +25% Flexibility
• +29% more likely to say they feel highly passionate about their work

“The data shows that employees using AI are having an all-around markedly better time on the job. They’re not just more productive; they’re experiencing greater excitement, fulfillment and pride in their work,” says Christina Janzer, head of the Workforce Lab. “Leaders should take note that using AI at work is correlated with a host of positive associations.”

That said, the data also shows that there’s a gap between executives’ urgency to incorporate AI tools and actual AI use among employees: the majority of desk workers —more than two-thirds—have still not tried AI for work.

So what’s stopping more employees from experimenting with AI tools? Concerns about privacy and data security followed by mistrust of data quality and accuracy top the list of blockers that desk workers cite as limiting factors. Just 7% of desk workers consider the outputs of AI completely trustworthy for work-related tasks, with 35% of desk workers saying AI results are only slightly or not at all trustworthy.

“Companies have urgent, ambitious goals for AI in the enterprise and our research shows there are huge productivity benefits to be gained—but many leaders are still figuring out how to kickstart adoption among employees,” says Denise Dresser, CEO of Slack. “While this is truly a seismic shift in the future of work, there are simple steps every business can take starting today to help onboard employees on AI while maintaining trust.”

What should leaders do to encourage uptake? The PET plan: Permission, education, training

Step one to boost workplace AI use is to clarify permission by establishing clear usage guidelines. This may sound obvious, yet nearly 2 in 5 desk workers (37%) say their company has no AI policy. Desk workers at companies that have established permissions for AI use are nearly 6x as likely to have experimented with AI tools.

The next step is education and training . Only a small percentage of global desk workers (15%) strongly agree that they have the education and training necessary to use AI effectively. Unsurprisingly, the more training and education workers have, the more likely they are to use AI tools, and those who are trained to use AI are up to 19x as likely to report that AI is improving their productivity.

Education and training are fundamental to building trust in AI tools; desk workers who are well trained in AI are 7x as likely to trust AI tools to assist them with work-related tasks compared with desk workers lacking AI training.

Another significant factor in a desk worker’s likelihood to try AI and to consider it trustworthy is whether that worker feels their manager trusts them as an employee. Desk workers who feel trusted by their employers are 94% more likely to have tried AI for work-related tasks, and they’re also more trusting of AI’s accuracy and reliability.

Forecasting the AI future: Three predictions for leaders

The ai hype cycle is far from peaking—it’s just gearing up..

You might think AI couldn’t possibly garner more hype. But sentiment from global desk workers indicates the buzz is just getting started. Today, 47% of global desk workers express enthusiasm for AI to handle tasks from their job (compared with 42% at the start of the year). The majority of desk workers—73%—believe the fanfare around AI is justified, saying they expect it to have a substantial impact on their work lives. This sentiment is even stronger among those who have firsthand experience using AI tools.

This trend is set to intensify as Gen Z and Gen Alpha enter the workforce. The youngest workers show the most enthusiasm for AI, with 55% of workers ages 18 to 29 saying they’re excited for AI and automation to handle parts of their work, compared with 33% of workers over age 60.

Our take: “AI fervor shows no signs of stopping,” says Nathalie Scardino, Salesforce’s Chief People Officer. “At Salesforce, we’ve seen how integrating AI into our workforce strategies can have massive benefits for employees and companies alike. Freeing up employees to focus on more impactful work is good for morale and for business.”

Mind the gap: AI could further marginalize women in the workforce—or give them a competitive edge.

There remains a small but stubborn gender gap in AI uptake, with more men trying AI for work (35% of respondents) compared with women (29% of respondents). Even though younger workers are most likely to have experimented with AI tools, the AI gender gap is largest among Gen Z, with men ages 18 to 29 25% more likely to have tried AI tools compared with women in the same age group.

One bright spot is that AI use is accelerating at a faster clip among workers of color, with 43% of Hispanic/Latinx desk workers, 42% of Black desk workers and 36% of Asian American desk workers having tried AI tools at work, compared with 29% of white desk workers. And there’s little to no gender gap among Hispanic/Latinx or Asian American employees.

Our take: “As we embrace our future with AI, it’s imperative that we continue to provide access to those who have historically been left out of technology shifts,” says Alexandra Legend Siegel, Salesforce’s Chief Equality Officer. “It is encouraging to see some of the findings on workplace AI tools and we’re committed to continuing to upskill and empower every community to leverage this technology for good in their workplace, careers and lives.”

AI is at risk of increasing busywork rather than reducing it.

AI promises to transform our work lives, and the latest Workforce Index survey shows there’s room for improvement: the majority of desk workers (64%) experience burnout once a month or more, about a third say they regularly feel stress about work, and 30% do not feel passion for their job. At the same time, desk workers estimate they spend about a third of their day, on average, on tasks they consider “low-value” and “not meaningful to their job.”

The top three most commonly cited low-value tasks are unnecessary meetings or work events, managing low-value emails, and excessive paperwork or data entry. In a perfect world, we’d minimize these tasks with the help of AI, creating more time to focus on more meaningful work.

But when asked how they would prioritize the time they get back from AI, “more admin” topped the list, while innovating and creating, learning and skill-building, and networking with colleagues fell to the bottom.

Our take: “AI could really open up a lot of time for workers, but it would be a shame if we waste that time on more busywork,” says Janzer. “If we want to make the most of what AI can offer, it’s up to us as leaders to help our people prioritize the most rewarding work.”

AI fast facts

Methodology

The survey included 10,045 workers in the U.S., Australia, France, Germany, Japan and the U.K. between March 6 and March 22, 2024.

The survey was administered by Qualtrics and did not target Slack or Salesforce employees or customers. Respondents were all desk workers, defined as employed full-time (30 or more hours per week) and either having one of the roles listed below or saying they “work with data, analyze information or think creatively”: executive management (e.g. president/partner, CEO, CFO, C-suite), senior management (e.g. executive VP, senior VP), middle management (e.g. department/group manager, VP), junior management (e.g. manager, team leader), senior staff (i.e. non-management), skilled office worker (e.g. analyst, graphic designer).

For brevity, we refer to the survey population as “desk-based” or “desk workers.”

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Mothers’ care is central factor in animal, human longevity

By caitlin hayes, cornell chronicle.

The relationship between mother and child may offer clues to the mystery of why humans live longer lives than expected for their size – and shed new light on what it means to be human.

“It’s one of the really mysterious things about humans, the fact that we live these super long lives as compared to so many other mammals,” said Matthew Zipple, Klarman Postdoctoral Fellow in neurobiology and behavior in the College of Arts and Sciences. “What we’re putting forward is that a part of the explanation for our long lifespan is this other foundational aspect of our lives, which is the relationship between the mother and her child.”

The paper, “ Maternal Care Leads to the Evolution of Long, Slow Lives ,” published in the Proceedings of the National Academy of Sciences on June 14.

In their models, Zipple and co-authors found consistently that in species where offspring survival depends on the longer-term presence of the mother, the species tends to evolve longer lives and a slower life pace, which is characterized by how long an animal lives and how often it reproduces.

“As we see these links between maternal survival and offspring fitness grow stronger, we see the evolution of animals having longer lives and reproducing less often – the same pattern we see in humans,” Zipple said. “And what’s nice about this model is that it’s general to mammals overall, because we know these links exist in other species outside of primates, like hyenas, whales and elephants.”

Zipple and co-authors provide a universal mathematical model that demonstrates the relationship between the maternal survival and fitness of offspring on the one hand, and on the other, pace of life. Two additional empirical models incorporate the types of data about maternal survival and offspring fitness collected by field ecologists. Zipple said the hope is that these models can be further tested and utilized by field ecologists to predict how maternal care and survival impacts the evolution of a species’ lifespan.

“We hope we’ve made the model straightforward enough, that field ecologists can take their existing long-term demographic data that they’ve been collecting for decades and apply it to this model, and come up with this estimate of how much they expect mother’s maternal care to have shaped the evolution of their study system,” Zipple said.

The work builds off the Mother and Grandmother hypothesis, based on observations in 18th- and 19th-century human populations, that offspring are more likely to survive if their mothers and grandmothers are in their lives. This theory has been used primarily as an explanation for menopause in humans, Zipple said – as ceasing reproduction decreases risk of death and allows older females to focus on grand-offspring care.

Zipple’s models are both broader and more specific, incorporating more of the ways that a mother’s presence or absence in her offspring’s life impacts its fitness. The team makes predictions, based on results from Zipple’s doctoral research on baboons and other primates, about how offspring fare if a mother dies after weaning but before the offspring’s sexual maturation, which Zipple found leads to short-term and long-term, even intergenerational, negative effects on primate offspring and grand-offspring.

“We wanted to expand the Mother and Grandmother hypothesis to look at these specific ways that we know, in primates, that the mother’s survival benefits her offspring,” Zipple said. “And ask what are the broader and perhaps more subtle ways in which the benefits of maternal presence in one’s life can lead to the evolution of longevity. We’re also trying to explain this phenomenon across a much wider range of animals.”

For Zipple, who spent six months during his doctoral research observing mother baboons with their infants in the field, the connection between motherhood and longevity reinforces his observations and underlines the importance and power of maternal care.

“When you watch mothers and infants in nonhuman primates interact, you can just see in the faces of the infants that there’s nothing more important in the world than the presence of its mother,” Zipple said. “So for me, the behavioral work, in combination with the demographic studies, really reinforced this common evolutionary thread that we share with our closest primate relatives – which is that there’s a period of time where the whole world is our mother, and while that gets weaker over time, it never goes away. Part of the long-term aspiration of this line of research is linking that to longevity, linking these two mysterious and central aspects of what it is to be human.”

Co-authors include H. Kern Reeve , professor of neurobiology and behavior (A&S), and Orca Jimmy Peniston, Kenai Peninsula College at the University of Alaska Anchorage.

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