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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

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

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

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Step 1: ask a question.

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

Step 2: Do some preliminary research

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

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

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

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

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

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

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

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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Researcher, Academic Writer, Web developer

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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."

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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What Is a Hypothesis? (Science)

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A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject.

In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.

In the study of logic, a hypothesis is an if-then proposition, typically written in the form, "If X , then Y ."

In common usage, a hypothesis is simply a proposed explanation or prediction, which may or may not be tested.

Writing a Hypothesis

Most scientific hypotheses are proposed in the if-then format because it's easy to design an experiment to see whether or not a cause and effect relationship exists between the independent variable and the dependent variable . The hypothesis is written as a prediction of the outcome of the experiment.

• Null Hypothesis and Alternative Hypothesis

Statistically, it's easier to show there is no relationship between two variables than to support their connection. So, scientists often propose the null hypothesis . The null hypothesis assumes changing the independent variable will have no effect on the dependent variable.

In contrast, the alternative hypothesis suggests changing the independent variable will have an effect on the dependent variable. Designing an experiment to test this hypothesis can be trickier because there are many ways to state an alternative hypothesis.

For example, consider a possible relationship between getting a good night's sleep and getting good grades. The null hypothesis might be stated: "The number of hours of sleep students get is unrelated to their grades" or "There is no correlation between hours of sleep and grades."

An experiment to test this hypothesis might involve collecting data, recording average hours of sleep for each student and grades. If a student who gets eight hours of sleep generally does better than students who get four hours of sleep or 10 hours of sleep, the hypothesis might be rejected.

But the alternative hypothesis is harder to propose and test. The most general statement would be: "The amount of sleep students get affects their grades." The hypothesis might also be stated as "If you get more sleep, your grades will improve" or "Students who get nine hours of sleep have better grades than those who get more or less sleep."

In an experiment, you can collect the same data, but the statistical analysis is less likely to give you a high confidence limit.

Usually, a scientist starts out with the null hypothesis. From there, it may be possible to propose and test an alternative hypothesis, to narrow down the relationship between the variables.

Example of a Hypothesis

Examples of a hypothesis include:

• If you drop a rock and a feather, (then) they will fall at the same rate.
• Plants need sunlight in order to live. (if sunlight, then life)
• Eating sugar gives you energy. (if sugar, then energy)
• White, Jay D.  Research in Public Administration . Conn., 1998.
• Schick, Theodore, and Lewis Vaughn.  How to Think about Weird Things: Critical Thinking for a New Age . McGraw-Hill Higher Education, 2002.
• Null Hypothesis Definition and Examples
• Definition of a Hypothesis
• What Are the Elements of a Good Hypothesis?
• Six Steps of the Scientific Method
• Independent Variable Definition and Examples
• What Are Examples of a Hypothesis?
• Understanding Simple vs Controlled Experiments
• Scientific Method Flow Chart
• Scientific Method Vocabulary Terms
• What Is a Testable Hypothesis?
• Null Hypothesis Examples
• What 'Fail to Reject' Means in a Hypothesis Test
• How To Design a Science Fair Experiment
• What Is an Experiment? Definition and Design
• Hypothesis Test for the Difference of Two Population Proportions

The Research Hypothesis: Role and Construction

• First Online: 01 January 2012

Cite this chapter

• Phyllis G. Supino EdD 3  

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A hypothesis is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator’s thinking about the problem and, therefore, facilitates a solution. There are three primary modes of inference by which hypotheses are developed: deduction (reasoning from a general propositions to specific instances), induction (reasoning from specific instances to a general proposition), and abduction (formulation/acceptance on probation of a hypothesis to explain a surprising observation).

A research hypothesis should reflect an inference about variables; be stated as a grammatically complete, declarative sentence; be expressed simply and unambiguously; provide an adequate answer to the research problem; and be testable. Hypotheses can be classified as conceptual versus operational, single versus bi- or multivariable, causal or not causal, mechanistic versus nonmechanistic, and null or alternative. Hypotheses most commonly entail statements about “variables” which, in turn, can be classified according to their level of measurement (scaling characteristics) or according to their role in the hypothesis (independent, dependent, moderator, control, or intervening).

A hypothesis is rendered operational when its broadly (conceptually) stated variables are replaced by operational definitions of those variables. Hypotheses stated in this manner are called operational hypotheses, specific hypotheses, or predictions and facilitate testing.

Wrong hypotheses, rightly worked from, have produced more results than unguided observation

—Augustus De Morgan, 1872[ 1 ]—

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Supino, P.G. (2012). The Research Hypothesis: Role and Construction. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_3

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Research Hypothesis In Psychology: Types, & Examples

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On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

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Biology library

Course: biology library   >   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..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• 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..

• Iteration time!
• 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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• 16 April 2024

US COVID-origins hearing puts scientific journals in the hot seat

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Brad Wenstrup (right), a Republican from Ohio who chairs the Select Subcommittee on the Coronavirus Pandemic, speaks with Raul Ruiz (left), a Democrat from California who is ranking member of the subcommittee. Credit: Al Drago/Bloomberg/Getty

During a public hearing in Washington DC today, Republicans in the US House of Representatives alleged that government scientists unduly influenced the editors of scientific journals and that, in turn, those publications stifled discourse about the origins of the COVID-19 pandemic. Democrats clapped back, lambasting their Republican colleagues for making such accusations without adequate evidence and for sowing distrust of science.

US congressional hearing produces heat but no light on COVID-origins debate

The session is the latest in a series of hearings held by the Select Subcommittee on the Coronavirus Pandemic to explore where the SARS-CoV-2 coronavirus came from, despite a lack of any new scientific evidence. Scientists have for some time been arguing over whether the virus spread naturally, from animals to people, or whether it leaked from a laboratory in Wuhan, China. Some have alleged that in the early days of the pandemic, government scientists Anthony Fauci, former director of the US National Institute of Allergy and Infectious Diseases, and Francis Collins, former director of the US National Institutes of Health (NIH), steered the scientific community, including journals, to dismiss the lab-leak hypothesis.

During the pandemic, “rather than journals being a wealth of information”, they instead “put a chilling effect on scientific research regarding the origins of COVID-19”, Brad Wenstrup, a Republican representative from Ohio who is chair of the subcommittee, said at the hearing. Raul Ruiz, a Democratic representative from California who is the ranking member of the subcommittee, shot back: “Congress should not be meddling in the peer-review process, and it should not be holding hearings to throw around baseless accusations.”

Holden Thorp, editor-in-chief of the Science family of journals in Washington DC, appeared before the committee to deny the suggestion that he had been coerced or censored by government scientists.

The subcommittee also invited Magdalena Skipper, Nature ’s editor-in-chief, and Richard Horton, editor-in-chief of the medical journal The Lancet , to appear, but neither was present. Skipper was absent owing to scheduling conflicts, but a spokesperson for Springer Nature says the company is “committed to remaining engaged with the Subcommittee and to assisting in its inquiry”. ( Nature ’s news team is editorially independent of its journals team and of its publisher, Springer Nature.) The Lancet did not respond to requests for comment.

Academic influence?

This is not the first time that Republicans have accused members of the scientific community of colluding with Fauci and Collins. Evolutionary biologist Kristian Andersen and virologist Robert Garry appeared before the same subcommittee on 11 July last year to deny allegations that the officials prompted them to publish a commentary in Nature Medicine 1 in March 2020 concluding that SARS-CoV-2 showed no signs of genetic engineering. They wrote in the journal that they did not “believe that any type of laboratory-based scenario is plausible” for the virus’s origins.

Holden Thorp became editor-in-chief of the Science family of journals in 2019. Credit: Steve Exum

Some lab-leak proponents have suggested, without evidence, that the pandemic began because the NIH funded risky coronavirus research at a lab in Wuhan, offering a motive for Collins and Fauci to promote a natural origin for COVID-19.

During the latest hearing, Republicans went a step further to suggest that not only did Collins and Fauci influence prominent biologists, but that they also encouraged journals to publish research supporting the natural-origin hypothesis. This accusation is based on e-mails that Wenstrup says the subcommittee obtained showing communication between top journal editors and government scientists. Thorp forcefully denied this line of questioning. “No government officials prompted or participated in the review or editing” of two key papers 2 , 3 on COVID-19’s origins published in Science , he testified. “Any papers supporting the lab-origin theory would go through the very same processes” of peer review as any other paper, he said.

Thorp otherwise spent much of the 80-minute hearing answering questions about how a scientific manuscript is prepared for publication, what a preprint is and how peer review works. In a tense moment, Wenstrup questioned a social-media post on Thorp’s personal X (formerly Twitter) page, in which he downplayed the lab-leak hypothesis. Thorp called the post “flippant” and apologised.

Communication queries

Correspondence between journal editors and government scientists is to be expected, Deborah Ross, a Democratic representative from North Carolina, said at the hearing. “Government actors querying academia on issues that are academic in nature isn’t malpractice or unlawful — it’s just doing their jobs.”

Anita Desikan, a senior analyst at the Union of Concerned Scientists who is based in Washington DC and focuses on scientific integrity, tells Nature’ s news team that it is customary for government agencies to reach out to stakeholders to inform policy decisions. Even if a government scientist suggests an idea for a journal paper, “that doesn’t mean it will be published or receive praise from the scientific community”.

Roger Pielke Jr, a science-policy researcher at the University of Colorado Boulder, who was originally slated to testify before the subcommittee until his invitation was rescinded owing to logistical reasons, disagrees. He thinks that Fauci and Collins still shaped the Nature Medicine COVID-19 origins paper by recommending that specific scientists investigate and by offering advice along the way. Nevertheless, the hearing was a “dud”, Pielke Jr says, because Thorp was the wrong witness. Instead, a more relevant witness would have been a government scientific-integrity officer who is more knowledgeable about what constitutes an ethical breach, he adds.

doi: https://doi.org/10.1038/d41586-024-01129-x

Andersen, K. G., Rambaut, A., Lipkin, W. I., Holmes E. C. & Garry, R. F. et al. Nature Med. 26 , 450–452 (2020).

Article   PubMed   Google Scholar  

Worobey, M. et al. Science 377 , 951–959 (2022).

Pekar, J. E. et al. Science 377 , 960–966 (2022).

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Taylor Swift Fans Are Theorizing That Her Song “The Albatross” Is Actually About All That Backlash She And Travis Kelce Faced At The Start Of Their Relationship — Here’s A Full Explainer

“‘You were sleeping soundly when they dragged you from your bed.’ Travis had to move out of his house when they started dating. She still thinks she brings down all her romantic partners and is a burden,” one fan speculated.

BuzzFeed Staff

Ever since Taylor Swift’s new album, The Tortured Poets Department , dropped on April 19, Swifties across the globe have continued speculating who each song is about.

If you didn’t know, Taylor famously encourages her fans to figure out who the subjects of her songs are by leaving hints and Easter eggs .

Despite this, lyrical analysis is entirely subjective, and there’s no way of actually confirming who Taylor is singing about in her songs — unless she reveals the info herself. That being said, one particular fan theory amid the release of TTPD has picked up steam online over the weekend.

Fans are speculating that Track 19, “The Albatross,” is not necessarily just about Taylor’s ex Joe Alwyn , as many had initially speculated , but also about her current boyfriend, Travis Kelce .

Taylor dated Joe for almost seven years, with news of their split being made public last April. Months later, after being briefly romantically linked to Matty Healy , Taylor began dating Kansas City Chiefs player Travis.

Taylor and Joe have never publicly acknowledged their breakup , but at the time, a source purportedly told People that he’d struggled with her heightened level of superstardom following the start of her record-breaking Eras Tour.

Fans speculated that some of the lyrics in “The Albatross” nodded toward Joe’s apparent dislike of the limelight . For example, Taylor sings , “You were sleeping soundly / When they dragged you from your bed / And I tried to warn you about them.”

She also sings, “Wise men once read fake news / And they believed it,” which may be a reference to the viral false rumor that she and Joe had secretly gotten married.

However, fans have since theorized that Taylor is actually singing about the backlash she and Travis faced at the start of their relationship.

Taylor and Travis went public with their relationship last September when the singer attended one of his football games. But sadly, the pair found themselves at the center of attention — not all positive — when the world learned that they were an item.

Taylor’s attendance at Travis’s games garnered widespread attention — not just from her fans but also from the NFL itself . After a while, people sadly began criticizing Taylor for simply attending the games, with some football fans even booing the singer at a Chiefs game in December.

During an interview with Time magazine that same month, Taylor acknowledged the immense attention — and scrutiny — that her attendance at Travis’s games had sparked. She said, “I’m just there to support Travis. I have no awareness of if I’m being shown too much and pissing off a few dads, Brads, and Chads.”

Meanwhile, Travis has publicly revealed that he and Taylor have had conversations about the “outside noise” surrounding their relationship.

“The only thing we’ve talked about is, as long as we’re happy, we can’t listen to anything that’s outside noise,” he said during a press conference in January. “That’s all that matters.”

A month later, Travis’s brother, Jason Kelce , revealed that the Chiefs player was forced to move houses due to “safety” concerns, explaining that while the pair have always been popular “in the football world,” Travis dating Taylor had introduced a bunch more fans who were on a “whole different level.”

While speaking of the increased attention on Travis in recent months, Jason said, “He had to completely move out of his house, right? People just staying by his house. I mean safety reasons. And the first day he moved into the new house, a gated community, somebody knocks on the back window of the house.”

With all of this in mind, several Swifties have since theorized that Taylor is singing about how Travis was hounded with newfound negative attention at the beginning of their relationship.

the albatross is about travis my god, i see it now too — tortured caro (@swiftpoetdep_) April 20, 2024

The Albatross being about Travis was NOT on my bingo card tbh #TSTTPD — Han🤍 🍉The Tortued Poet ⸆⸉ (@tisthehanszn13) April 19, 2024

Someone said The Albatross is the public and her exes warning Travis about her and I’m never gonna know peace again — Cass (@Cass89118) April 21, 2024

Taylor sings , “Cautions issued, he stood / Shooting the messengers / They tried to warn him about her.” She adds, “She's the albatross / She is here to destroy you.”

“You were sleeping soundly / When they dragged you from your bed / And I tried to warn you about them,” she sings, with fans speculating that this is in reference to Travis having to move from his home.

In a popular TikTok video , one user questioned, “Why is no one talking about how 'The Albatross' is also a Travis song? … Everybody was telling Travis, ‘Be careful, don’t get with her. She’ll ruin your career.’”

“Everybody was so pissed when they saw her on their screen,” the user said, referring to the backlash over Taylor’s attendance at Travis’s games. “They were saying, ‘She’s gonna destroy you.’”

“I think the albatross is about Taylor and Travis and him being warned off of her but her chose her,” someone agreed on X, formerly known as Twitter.

“I might be crazy but…why does the albatross feel like her writing about what people were saying to Travis when they first started,” another person guessed .

“Ok on the Albatross she [says], ‘you were sleeping soundly when they dragged you from your bed.’ Travis had to move out of his house when they started dating. She still thinks she brings down all her romantic partners and is a burden,” one more user said .

Again, this theory is purely speculation, and Taylor has not given any explicit information on who the song is about.

You can listen to all of TTPD here .

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• Taylor Swift
• Travis Kelce

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Ask NYT Climate

Is Online Shopping Bad for the Planet?

In theory, getting deliveries can be more efficient than driving to the store. But you may still want to think before you add to cart.

Credit... Naomi Anderson-Subryan

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By Dionne Searcey

Dionne Searcey is part of a rotating cast of Climate reporters and special guest writers who will answer your burning climate questions.

• April 22, 2024

Q: How much do I need to worry about the impact of my online shopping?

The convenience of online shopping is hard to beat. But it uses a lot of energy and resources and can lead to more waste.

Transportation needed for online shopping spews greenhouse emissions. Three billion trees are cut down every year to produce packaging for all kinds of things, e-commerce included, according to some estimates . The data centers needed to store and retrieve orders consume about 10 times the amount of energy of a typical home and gulp precious groundwater for cooling.

Sounds bad, right? Read on.

Online shopping isn’t always the worst choice. Efficiency is a big factor.

Think of it like this: A single truck delivering orders to several homes could be less of a drain on the environment than several shoppers hopping in cars to drive to stores. That’s especially true if people group their purchases into less-frequent deliveries.

One study from M.I.T . even found that online shopping could be more sustainable than traditional shopping in more than 75 percent of scenarios that researchers came up with. Those scenarios imagined things like an online shopping experience with all-electric shipping and reduced packaging.

Online retailers and delivery companies have been trying to make online shopping more climate friendly. Some have embraced electric vehicles.

Amazon.com, for instance, has pledged to have 100,000 electric delivery vehicles on the road by 2030, a move that it says will prevent millions of metric tons of planet-warming carbon from being released into the atmosphere. UPS has plans for updating its fleet with electric vehicles, but those plans hit a snag when the company it had contracted to provide the new trucks ran into financial problems . FedEx plans to convert its entire parcel pickup and delivery fleet to E.V.s by 2040, with plans for half of its fleet to be electrified by next year.

Some companies are also experimenting with robot and drone deliveries . But there are other things to consider.

Packaging and waste are also important.

Companies like Amazon have also started to cut back on packaging, which in the early days of online shopping produced laughable mountains of boxes, Bubble Wrap and other padding for tiny items. It still happens from time to time now , even with the effort to reduce. Some companies have begun using more reusable, recyclable and even biodegradable packaging. But millions of pounds of plastic from packaging still end up in rivers, oceans and landfills.

Maybe the biggest thing: How much stuff we buy.

So, it’s complicated. But there’s one foolproof thing you can do for the planet and for your bank account: Buy less stuff.

The production and use of household goods and services are responsible for 60 percent of greenhouse gas emissions worldwide, one 2015 study found . In the United States, more than 20 percent of emissions are directly attributed to household consumption, according to researchers at the University of Michigan .

Many of those lamps, toasters, sweaters and other items are imported, arriving in the United States on carbon-emitting cargo ships or airplanes. The shipping industry alone accounts for 3 percent of global greenhouse gas emissions.

Things to try: Buying in bulk, slow shopping and bundling orders.

Climate organizations encourage buying secondhand items or fixing the broken things you already own. An increasing number of companies offer repair services, sometimes for free. YouTube videos offer step-by-step guides for fixing a surprising number of items. Local meet-ups for mending clothing or repairing appliances are becoming a thing.

If you are going to buy stuff online, there are many ways you can make your online shopping more sustainable.

Take a minute to look at size charts and read reviews to cut back on returns. Many studies say online shoppers are five times more likely to return an item, which means a lot more transportation emissions.

If you’re ordering several items, try to group your orders into one shipment. Many companies will ask if you want to do so; don’t forget to seek out that option. The Better Business Bureau suggests buying in bulk to cut down on packaging for individual items and taking advantage of delivery to pickup locations.

Practice slow shopping . Pause and think about whether you need an item. It’s easy to get a rush from buying something new, but environmentalists suggest getting your dopamine fix from something entirely different: Try taking a walk instead.

Have a question for reporters covering climate and the environment?

We might answer your question in a future column. We won’t publish your submission without contacting you, and may use your contact information to follow up with you.

Dionne Searcey is a Times reporter who writes about how the choices made by people and corporations affect the future of the planet. More about Dionne Searcey

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MTG nodded to her infamous 'Jewish space laser' theory in an amendment meant to mess up sending aid to Israel

• Rep. Marjorie Taylor Greene revisited tricky territory in an amendment to an Israel funding bill.
• Greene's amendment would divert cash from Israel to developing space lasers for the US border.
• A 2018 post suggesting that lasers in space caused a California wildfire has long haunted Greene.

Rep. Marjorie Taylor Greene made clear her opposition to sending extra money to Israel in a weirdly self-referential way.

The Georgia Republican filed an amendment to a $26.38 billion Israel aid bill that would divert some of that funding toward "the development of space laser technology on the southwest border" of the United States.

It was a not-so-subtle reference to a lowlight of her political past, when she suggested in a 2018 Facebook post that a Jewish-financed laser beam ignited one of the worst wildfires in California's history.

"I've previously voted to fund space lasers for Israel's defense," said Greene. "America needs to take our national security seriously and deserves the same type of defense for our border that Israel has and proudly uses."

The bill includes $1.2 billion for Israel's experimental Iron Beam system.

Israel has some of the best unmanned defense systems in the world. I’ve previously voted to fund space lasers for Israel’s defense. America needs to take our national security seriously and deserves the same type of defense for our border that Israel has and proudly uses. pic.twitter.com/oDeDqTXvQQ — Rep. Marjorie Taylor Greene🇺🇸 (@RepMTG) April 18, 2024

Greene's old conspiratorial social media rants were uncovered by outlets like CNN and Media Matters after she first took office in 2021.

Related stories

In the case of the "Jewish space lasers," she connected the wildfire to the Rothschild family of Jewish financiers, a favorite target of antisemitic conspiracy theories.

The "Jewish space laser" theory has become a staple piece of mockery for those attacking Greene, and is often brought up by reporters.

A video from early March shows Greene being less than thrilled to talk about the theory, telling British reporter Emily Maitlis to "fuck off" after she asked about it.

It might seem strange, then, for Greene to voluntarily go back to space-laser territory, given that it's often earned her mockery. But it's also in keeping with her MAGA Republican style, embracing the most eye-catching possible methods to signal her positions — in this case, her die-hard opposition to sending aid to other countries.

The suggestion isn't realistic and isn't meant to be — it was among a raft of amendments offered by Greene and allies like Rep. Paul Gosar of Arizona to make it harder to pass the bills.

Greene's office has not yet responded to a request for comment sent outside regular business hours.

The House is set to vote this weekend on a package of bills to send aid to Ukraine, Israel, and Taiwan — but in contrast to a $95.3 billion bill passed by the Senate two months ago, House members will take individual votes on each component.

President Joe Biden on Wednesday said that he supports the measures , and urged the House and Senate to quickly pass them so he could sign them into law.

Greene has voted against Israel aid in the past, and she's long been one of the most outspoken opponents of Ukraine aid . She also introduced seven amendments to the Ukraine aid bill, including provisions that would divert money to US disaster zones or force any members of Congress who support the bill to enlist in the Ukrainian military.

Watch: The House floor showdown between Lauren Boebert and Marjorie Taylor Greene is just the tip of the iceberg

• Main content

Man throws conspiracy theory pamphlets in the air before setting himself on fire outside Trump trial

The man is in a critical condition after setting himself alight outside the Manhattan courthouse while the former US president was inside.

News reporter @TomGillespie1

Saturday 20 April 2024 06:05, UK

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A man has thrown conspiracy theory pamphlets in the air before setting himself on fire outside the New York courthouse where former US President Donald Trump is on trial.

Police have identified the man as Maxwell Azzarello, in his mid-30s, from St Augustine in Florida.

He is now in critical condition in hospital after setting himself alight in a designated protest area for pro and anti-Trump demonstrators.

The NYPD said it has opened an investigation after its officers responded to the fire at around 1:37pm local time on Friday (6:37pm UK time).

Mr Azzarello took a canister out of his bag containing what is believed to have been an alcohol-based accelerant, before dousing himself in the fluid and setting himself ablaze, police said.

Officers and civilians ran into the protest area and attempted to put out the flames using coats and fire extinguishers, NYPD Chief of Department Jeff Maddrey told reporters.

Four police officers suffered minor injuries from fire exposure, authorities said.

More on Donald Trump

Trump on trial: Porn stars, hush money and a presidential election

Porn stars, sex scandals and zzzs: The A to Z of Trump's hush money trial

Donald Trump trial: Possible jurors in tears and excused for anxiety as selection concludes

Related Topics:

• Donald Trump

NYPD Chief of Detectives Joseph Kenny told reporters: "The pamphlets appear to be propaganda-based, almost a conspiracy theory type of pamphlet. Some information in regards to a Ponzi scheme and the fact that some of our local educational institutes are fronts for the mob. So, a little bit of a conspiracy theory going on here."

The blaze broke out after jury selection for Trump's hush money trial concluded with 12 people, and six alternatives, chosen to decide whether the former US president covered up payments to women who alleged they had affairs with him.

The Trump campaign released a statement on Friday afternoon offering its "condolences to the traumatised witnesses" after the blaze.

Read more: Who is the porn star at the centre of Trump's hush money case? Trump blasts trial as a 'disgrace'

Karoline Leavitt, the national secretary for his campaign, said: "Not knowing the motivations behind this sickening situation, it's difficult to make any definitive remarks, other than to say we are thankful that to the best of our present knowledge, nobody other than the individual in question was hurt."

She added: "Today is more proof that our nation is in deep trouble... Make America Great Again."

Footage shared on social media shows Mr Azzarello lying on his back on the pavement outside the courthouse while the lower part of his body is on fire.

Another man sprays him with a fire extinguisher which appears to put the fire out.

Police officers are seen running over to the scene as the fire is extinguished.

Freelance photojournalist Ed Quinn was outside the court at the time and told Sky News' US partner network NBC News: "I heard someone scream 'He's going to light himself on fire!'.'

"I see him dumping gasoline on his face, very deliberately.

"He had a grey T-shirt on. It soaked his face. It soaked his shirt. Boom, he went up."

Mr Quinn said it took the police about a minute to arrive.

He continued: "Women were begging, screaming, put it out, put him out."

Trump facing multiple charges in historic trial

Hours after the jury members were decided on Friday, an appeals court judge rejected a last-minute bid by Trump to halt the trial over his claims that the jury selection process was unfairly rushed.

Be the first to get Breaking News

Install the Sky News app for free

The judge also said he would not consider Trump's immunity motion that was filed just before the hush money trial began.

Opening statements in the trial are scheduled to take place on Monday.

In what is the first criminal trial of a former US president, Trump is accused of criminally altering business records to cover up a $130,000 (£104,200) payment to adult film actress Stormy Daniels, real name Stephanie Clifford, during his 2016 election campaign.

Ms Daniels and former Playboy model Karen McDougal, who was paid $150,000 (£120,000), both claim to have had affairs with Trump.

His lawyers say the payment was meant to spare himself and his family embarrassment, not help him win the election.

Click to subscribe to the Sky News Daily wherever you get your podcasts

Trump is facing 34 felony counts of falsifying business records and could get up to four years in prison if convicted.

He is also facing three other criminal cases that could go to trial.

Trump has pleaded not guilty to all charges.

In court, one prospective juror was excused after saying she suffers from anxiety and felt she could struggle to be impartial, while another was called to the judge's bench after bursting into tears.

The first woman said she takes medication and that as the days go on "I don't think I will be able to be completely fair".

The second broke down crying and, according to Sky News' partner NBC News, said: "I have to be honest, I feel so nervous and anxious right now. I'm sorry.

"I thought I could do this... I don't want you to feel like I've wasted anyone's time," she added before being called to the bench and excused."

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Hypothesis testing, p values, confidence intervals, and significance.

Jacob Shreffler ; Martin R. Huecker .

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Last Update: March 13, 2023 .

• Definition/Introduction

Medical providers often rely on evidence-based medicine to guide decision-making in practice. Often a research hypothesis is tested with results provided, typically with p values, confidence intervals, or both. Additionally, statistical or research significance is estimated or determined by the investigators. Unfortunately, healthcare providers may have different comfort levels in interpreting these findings, which may affect the adequate application of the data.

• Issues of Concern

Without a foundational understanding of hypothesis testing, p values, confidence intervals, and the difference between statistical and clinical significance, it may affect healthcare providers' ability to make clinical decisions without relying purely on the research investigators deemed level of significance. Therefore, an overview of these concepts is provided to allow medical professionals to use their expertise to determine if results are reported sufficiently and if the study outcomes are clinically appropriate to be applied in healthcare practice.

Hypothesis Testing

Investigators conducting studies need research questions and hypotheses to guide analyses. Starting with broad research questions (RQs), investigators then identify a gap in current clinical practice or research. Any research problem or statement is grounded in a better understanding of relationships between two or more variables. For this article, we will use the following research question example:

Research Question: Is Drug 23 an effective treatment for Disease A?

Research questions do not directly imply specific guesses or predictions; we must formulate research hypotheses. A hypothesis is a predetermined declaration regarding the research question in which the investigator(s) makes a precise, educated guess about a study outcome. This is sometimes called the alternative hypothesis and ultimately allows the researcher to take a stance based on experience or insight from medical literature. An example of a hypothesis is below.

Research Hypothesis: Drug 23 will significantly reduce symptoms associated with Disease A compared to Drug 22.

The null hypothesis states that there is no statistical difference between groups based on the stated research hypothesis.

Researchers should be aware of journal recommendations when considering how to report p values, and manuscripts should remain internally consistent.

Regarding p values, as the number of individuals enrolled in a study (the sample size) increases, the likelihood of finding a statistically significant effect increases. With very large sample sizes, the p-value can be very low significant differences in the reduction of symptoms for Disease A between Drug 23 and Drug 22. The null hypothesis is deemed true until a study presents significant data to support rejecting the null hypothesis. Based on the results, the investigators will either reject the null hypothesis (if they found significant differences or associations) or fail to reject the null hypothesis (they could not provide proof that there were significant differences or associations).

To test a hypothesis, researchers obtain data on a representative sample to determine whether to reject or fail to reject a null hypothesis. In most research studies, it is not feasible to obtain data for an entire population. Using a sampling procedure allows for statistical inference, though this involves a certain possibility of error. [1]  When determining whether to reject or fail to reject the null hypothesis, mistakes can be made: Type I and Type II errors. Though it is impossible to ensure that these errors have not occurred, researchers should limit the possibilities of these faults. [2]

Significance

Significance is a term to describe the substantive importance of medical research. Statistical significance is the likelihood of results due to chance. [3]  Healthcare providers should always delineate statistical significance from clinical significance, a common error when reviewing biomedical research. [4]  When conceptualizing findings reported as either significant or not significant, healthcare providers should not simply accept researchers' results or conclusions without considering the clinical significance. Healthcare professionals should consider the clinical importance of findings and understand both p values and confidence intervals so they do not have to rely on the researchers to determine the level of significance. [5]  One criterion often used to determine statistical significance is the utilization of p values.

P values are used in research to determine whether the sample estimate is significantly different from a hypothesized value. The p-value is the probability that the observed effect within the study would have occurred by chance if, in reality, there was no true effect. Conventionally, data yielding a p<0.05 or p<0.01 is considered statistically significant. While some have debated that the 0.05 level should be lowered, it is still universally practiced. [6]  Hypothesis testing allows us to determine the size of the effect.

An example of findings reported with p values are below:

Statement: Drug 23 reduced patients' symptoms compared to Drug 22. Patients who received Drug 23 (n=100) were 2.1 times less likely than patients who received Drug 22 (n = 100) to experience symptoms of Disease A, p<0.05.

Statement:Individuals who were prescribed Drug 23 experienced fewer symptoms (M = 1.3, SD = 0.7) compared to individuals who were prescribed Drug 22 (M = 5.3, SD = 1.9). This finding was statistically significant, p= 0.02.

For either statement, if the threshold had been set at 0.05, the null hypothesis (that there was no relationship) should be rejected, and we should conclude significant differences. Noticeably, as can be seen in the two statements above, some researchers will report findings with < or > and others will provide an exact p-value (0.000001) but never zero [6] . When examining research, readers should understand how p values are reported. The best practice is to report all p values for all variables within a study design, rather than only providing p values for variables with significant findings. [7]  The inclusion of all p values provides evidence for study validity and limits suspicion for selective reporting/data mining.  

While researchers have historically used p values, experts who find p values problematic encourage the use of confidence intervals. [8] . P-values alone do not allow us to understand the size or the extent of the differences or associations. [3]  In March 2016, the American Statistical Association (ASA) released a statement on p values, noting that scientific decision-making and conclusions should not be based on a fixed p-value threshold (e.g., 0.05). They recommend focusing on the significance of results in the context of study design, quality of measurements, and validity of data. Ultimately, the ASA statement noted that in isolation, a p-value does not provide strong evidence. [9]

When conceptualizing clinical work, healthcare professionals should consider p values with a concurrent appraisal study design validity. For example, a p-value from a double-blinded randomized clinical trial (designed to minimize bias) should be weighted higher than one from a retrospective observational study [7] . The p-value debate has smoldered since the 1950s [10] , and replacement with confidence intervals has been suggested since the 1980s. [11]

Confidence Intervals

A confidence interval provides a range of values within given confidence (e.g., 95%), including the accurate value of the statistical constraint within a targeted population. [12]  Most research uses a 95% CI, but investigators can set any level (e.g., 90% CI, 99% CI). [13]  A CI provides a range with the lower bound and upper bound limits of a difference or association that would be plausible for a population. [14]  Therefore, a CI of 95% indicates that if a study were to be carried out 100 times, the range would contain the true value in 95, [15]  confidence intervals provide more evidence regarding the precision of an estimate compared to p-values. [6]

In consideration of the similar research example provided above, one could make the following statement with 95% CI:

Statement: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22; there was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).

It is important to note that the width of the CI is affected by the standard error and the sample size; reducing a study sample number will result in less precision of the CI (increase the width). [14]  A larger width indicates a smaller sample size or a larger variability. [16]  A researcher would want to increase the precision of the CI. For example, a 95% CI of 1.43 – 1.47 is much more precise than the one provided in the example above. In research and clinical practice, CIs provide valuable information on whether the interval includes or excludes any clinically significant values. [14]

Null values are sometimes used for differences with CI (zero for differential comparisons and 1 for ratios). However, CIs provide more information than that. [15]  Consider this example: A hospital implements a new protocol that reduced wait time for patients in the emergency department by an average of 25 minutes (95% CI: -2.5 – 41 minutes). Because the range crosses zero, implementing this protocol in different populations could result in longer wait times; however, the range is much higher on the positive side. Thus, while the p-value used to detect statistical significance for this may result in "not significant" findings, individuals should examine this range, consider the study design, and weigh whether or not it is still worth piloting in their workplace.

Similarly to p-values, 95% CIs cannot control for researchers' errors (e.g., study bias or improper data analysis). [14]  In consideration of whether to report p-values or CIs, researchers should examine journal preferences. When in doubt, reporting both may be beneficial. [13]  An example is below:

Reporting both: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22, p = 0.009. There was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).

• Clinical Significance

Recall that clinical significance and statistical significance are two different concepts. Healthcare providers should remember that a study with statistically significant differences and large sample size may be of no interest to clinicians, whereas a study with smaller sample size and statistically non-significant results could impact clinical practice. [14]  Additionally, as previously mentioned, a non-significant finding may reflect the study design itself rather than relationships between variables.

Healthcare providers using evidence-based medicine to inform practice should use clinical judgment to determine the practical importance of studies through careful evaluation of the design, sample size, power, likelihood of type I and type II errors, data analysis, and reporting of statistical findings (p values, 95% CI or both). [4]  Interestingly, some experts have called for "statistically significant" or "not significant" to be excluded from work as statistical significance never has and will never be equivalent to clinical significance. [17]

The decision on what is clinically significant can be challenging, depending on the providers' experience and especially the severity of the disease. Providers should use their knowledge and experiences to determine the meaningfulness of study results and make inferences based not only on significant or insignificant results by researchers but through their understanding of study limitations and practical implications.

• Nursing, Allied Health, and Interprofessional Team Interventions

All physicians, nurses, pharmacists, and other healthcare professionals should strive to understand the concepts in this chapter. These individuals should maintain the ability to review and incorporate new literature for evidence-based and safe care. 

• Review Questions
• Access free multiple choice questions on this topic.
• Comment on this article.

Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Shreffler J, Huecker MR. Hypothesis Testing, P Values, Confidence Intervals, and Significance. [Updated 2023 Mar 13]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Walton Goggins’ Fallout character might have actually appeared in the games, at least according to this fan theory

It's now our new headcanon

Fallout fans have spotted a new similarity between a Fallout: New Vegas character and one of the leads of the TV show, and now we can’t unsee it. 

Walton Goggins’ The Ghoul isn’t based on any one character in the Fallout franchise, but some viewers have wondered if we may have encountered him before. Posting on Reddit , one player pointed out that Goggins’ Cooper Howard, who worked as an actor in westerns in flashback scenes, seems an awful lot like Victor. 

The Securitron AI is a mainstay of Fallout: New Vegas, appearing as a cowboy-like image on the security bots across the Mojave Wasteland in 2281. The resemblance between the two is pretty uncanny, to start with, and some other fans have chimed in with their own theories about just how they might be linked and calling it their new headcanon. "Fuck it. People sell their likenesses all the time. That’s a great Headcanon," wrote one , while another added : "That’s… the best series theory I’ve heard so far!"

In fact, one theory even suggests this may actually be Cooper Howard, and it’s all down to Matt Berry’s character, Sebastian Leslie. In a flashback scene during episode six , Leslie tells Howard that Vault-Tec paid him to voice their Mister Handy series of robots. This is why he’s the voice of Codsworth elsewhere in the series. Given that Howard seemingly worked with Vault-Tec several times, could he have done the same thing?

"Cooper could have pursued this line of work, and Robert House (founder/owner of RobCo, among other things) certainly had the capital, as well as the social connection to Cooper through Sebastian/RobCo, to hire Cooper to do the voicework for Victor in his typical western character. Cigarette and all," one fan suggests , and it makes a lot of sense.

Given that the season one finale seemingly set up New Vegas as a big location for Fallout season 2 , this has left us wondering if a season two mash-up might be on the way. It would certainly be a lot of fun for Goggins to voice Victor if we met him in the show, and even better if we saw him encounter the robot as The Ghoul. Make it happen, Prime Video.

For more on Fallout, check out our spoiler-filled guides to:

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I’m the Deputy Entertainment Editor here at GamesRadar+, covering TV and film for the Total Film and SFX sections online. I previously worked as a Senior Showbiz Reporter and SEO TV reporter at Express Online for three years. I've also written for The Resident magazines and Amateur Photographer, before specializing in entertainment.

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