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What is a Hypothesis – Types, Examples and Writing Guide

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Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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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.

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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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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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• How to Write a 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 .

Prevent plagiarism, run a free check.

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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Definition of a Hypothesis

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A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.

In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.

Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true. 

Null Hypothesis

A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.

Alternative Hypothesis

Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.

Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.

Formulating a Hypothesis

Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.

Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

Updated by Nicki Lisa Cole, Ph.D

• Null Hypothesis Examples
• Examples of Independent and Dependent Variables
• Difference Between Independent and Dependent Variables
• What Is a Hypothesis? (Science)
• Understanding Path Analysis
• What Are the Elements of a Good Hypothesis?
• What It Means When a Variable Is Spurious
• What 'Fail to Reject' Means in a Hypothesis Test
• How Intervening Variables Work in Sociology
• Null Hypothesis Definition and Examples
• Understanding Simple vs Controlled Experiments
• Scientific Method Vocabulary Terms
• Null Hypothesis and Alternative Hypothesis
• Six Steps of the Scientific Method
• What Are Examples of a Hypothesis?
• Structural Equation Modeling

How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

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It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

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It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

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5 Basic Steps in Formulation of Hypothesis in Research

Formulation of a Hypothesis in research is an essential task in the entire Research Process that comes in the third step. A hypothesis is a tentative solution to a research problem or question. Here, we will cover a functional definition of a hypothesis & basic Steps in the formulation of hypotheses for your research.

Research works, in fact, are designed to verify the hypothesis. Therefore, a researcher, of course, would understand the meaning and nature of the hypothesis in order to formulate a hypothesis and then test the hypothesis.

What is Hypothesis in Research?

A hypothesis is a tentative statement of a proposition that the researcher seeks to prove. It’s basically a concrete generalization. Of course, this generalization requires essential characteristics that pertain to an entire class of phenomena.

When a theory is stated as a testable proposition formally and subjects to empirical verification we can define it as a hypothesis. Researchers make a hypothesis on the basis of some earlier theories and some rationale that is generally accepted as true. The hypothesis test finally will decide whether it is true or rejected.

So, to clarify a hypothesis is a statement about the relationship between two or more variables. The researcher set out the variables to prove or disprove. Hypothesis essentially includes three elements. For example-

• Relationship between variables.

Example of Hypothesis

• Rewards increase reading achievements
• Rewards decrease reading achievements
• Or rewards have no effect on reading achievements

In the above examples- variables are- Rewards & Achievements.

Steps in Formulation of Hypothesis

A hypothesis is a tentative assumption drawn from practical knowledge or theory. A hypothesis is used as a guide in the inquiry of other facts or theories that a researcher does not know. However, the formulation of the hypothesis is one of the most difficult steps in the entire scientific research process.

Therefore, in this regard, we intend to point out the basic steps in the formulation of a hypothesis. We are pretty sure that this guideline will be helpful in your research work.

1. Define Variables

At first, with a view to formulating a hypothesis, you must define your variables. What do you want to test? Will you test that rewards increase reading achievement? Or do rewards decrease reading achievement? Whatever your goals are, they need to be clearly defined, quantifiable, and measurable. This will provide you with a clear idea of what to follow to achieve results.

2. Study In-Depth the Variables

If we do think that your variables are Rewards & Achievements, then you need to intense study how rewards increase reading achievements? An in-depth study, rigorous questions, and data of rewards increase reading achievements will make you able to confirm your hypothesis. Specify dependent and independent variables.

3. Specify the Nature of the Relationship

Then, identify what relationship there exist between the variables. What variable influences the other? That is what is the dependent variable and what is the independent variable? How do Rewards impact achievements? If reward plays a key role in reading achievements, then reward is the independent variable.

4. Identify Study Population

The population in research means the entire group of individuals is going to study. If you want to test how rewards increase reading achievements in the United Kingdom, you need not study the whole population of the United Kingdom. Because the total population does not involve in reading achievements. Therefore, the researcher must identify the study population.

5. Make Sure Variables are Testable

Variables in your hypothesis must be testable. Otherwise, the hypothesis would be worthless. Because your research study must accept or reject a variable. So, variables you must need to test. Testable variables can only be accepted or rejected. Moreover, the sole aim of a research hypothesis is to test variables in the long run.

How to Choose a Research Design?

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• Scientific Methods

What is Hypothesis?

We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.

A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The hypothesis should be clear and precise to consider it to be reliable.
• If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.
• The hypothesis must be specific and should have scope for conducting more tests.
• The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.

Sources of Hypothesis

Following are the sources of hypothesis:

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

Types of Hypothesis

There are six forms of hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

• Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.
• All lilies have the same number of petals is an example of a null hypothesis.
• If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

Functions of Hypothesis

Following are the functions performed by the hypothesis:

• Hypothesis helps in making an observation and experiments possible.
• It becomes the start point for the investigation.
• Hypothesis helps in verifying the observations.
• It helps in directing the inquiries in the right direction.

How will Hypothesis help in the Scientific Method?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Formation of question
• Doing background research
• Creation of hypothesis
• Designing an experiment
• Collection of data
• Result analysis
• Summarizing the experiment
• Communicating the results

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction.

Define complex hypothesis..

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

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Formulation of Research Question – Stepwise Approach

Simmi k. ratan.

Department of Pediatric Surgery, Maulana Azad Medical College, New Delhi, India

1 Department of Community Medicine, North Delhi Municipal Corporation Medical College, New Delhi, India

2 Department of Pediatric Surgery, Batra Hospital and Research Centre, New Delhi, India

Formulation of research question (RQ) is an essentiality before starting any research. It aims to explore an existing uncertainty in an area of concern and points to a need for deliberate investigation. It is, therefore, pertinent to formulate a good RQ. The present paper aims to discuss the process of formulation of RQ with stepwise approach. The characteristics of good RQ are expressed by acronym “FINERMAPS” expanded as feasible, interesting, novel, ethical, relevant, manageable, appropriate, potential value, publishability, and systematic. A RQ can address different formats depending on the aspect to be evaluated. Based on this, there can be different types of RQ such as based on the existence of the phenomenon, description and classification, composition, relationship, comparative, and causality. To develop a RQ, one needs to begin by identifying the subject of interest and then do preliminary research on that subject. The researcher then defines what still needs to be known in that particular subject and assesses the implied questions. After narrowing the focus and scope of the research subject, researcher frames a RQ and then evaluates it. Thus, conception to formulation of RQ is very systematic process and has to be performed meticulously as research guided by such question can have wider impact in the field of social and health research by leading to formulation of policies for the benefit of larger population.

I NTRODUCTION

A good research question (RQ) forms backbone of a good research, which in turn is vital in unraveling mysteries of nature and giving insight into a problem.[ 1 , 2 , 3 , 4 ] RQ identifies the problem to be studied and guides to the methodology. It leads to building up of an appropriate hypothesis (Hs). Hence, RQ aims to explore an existing uncertainty in an area of concern and points to a need for deliberate investigation. A good RQ helps support a focused arguable thesis and construction of a logical argument. Hence, formulation of a good RQ is undoubtedly one of the first critical steps in the research process, especially in the field of social and health research, where the systematic generation of knowledge that can be used to promote, restore, maintain, and/or protect health of individuals and populations.[ 1 , 3 , 4 ] Basically, the research can be classified as action, applied, basic, clinical, empirical, administrative, theoretical, or qualitative or quantitative research, depending on its purpose.[ 2 ]

Research plays an important role in developing clinical practices and instituting new health policies. Hence, there is a need for a logical scientific approach as research has an important goal of generating new claims.[ 1 ]

C HARACTERISTICS OF G OOD R ESEARCH Q UESTION

“The most successful research topics are narrowly focused and carefully defined but are important parts of a broad-ranging, complex problem.”

A good RQ is an asset as it:

• Details the problem statement
• Further describes and refines the issue under study
• Adds focus to the problem statement
• Guides data collection and analysis
• Sets context of research.

Hence, while writing RQ, it is important to see if it is relevant to the existing time frame and conditions. For example, the impact of “odd-even” vehicle formula in decreasing the level of air particulate pollution in various districts of Delhi.

A good research is represented by acronym FINERMAPS[ 5 ]

Interesting.

• Appropriate
• Potential value and publishability
• Systematic.

Feasibility means that it is within the ability of the investigator to carry out. It should be backed by an appropriate number of subjects and methodology as well as time and funds to reach the conclusions. One needs to be realistic about the scope and scale of the project. One has to have access to the people, gadgets, documents, statistics, etc. One should be able to relate the concepts of the RQ to the observations, phenomena, indicators, or variables that one can access. One should be clear that the collection of data and the proceedings of project can be completed within the limited time and resources available to the investigator. Sometimes, a RQ appears feasible, but when fieldwork or study gets started, it proves otherwise. In this situation, it is important to write up the problems honestly and to reflect on what has been learned. One should try to discuss with more experienced colleagues or the supervisor so as to develop a contingency plan to anticipate possible problems while working on a RQ and find possible solutions in such situations.

This is essential that one has a real grounded interest in one's RQ and one can explore this and back it up with academic and intellectual debate. This interest will motivate one to keep going with RQ.

The question should not simply copy questions investigated by other workers but should have scope to be investigated. It may aim at confirming or refuting the already established findings, establish new facts, or find new aspects of the established facts. It should show imagination of the researcher. Above all, the question has to be simple and clear. The complexity of a question can frequently hide unclear thoughts and lead to a confused research process. A very elaborate RQ, or a question which is not differentiated into different parts, may hide concepts that are contradictory or not relevant. This needs to be clear and thought-through. Having one key question with several subcomponents will guide your research.

This is the foremost requirement of any RQ and is mandatory to get clearance from appropriate authorities before stating research on the question. Further, the RQ should be such that it minimizes the risk of harm to the participants in the research, protect the privacy and maintain their confidentiality, and provide the participants right to withdraw from research. It should also guide in avoiding deceptive practices in research.

The question should of academic and intellectual interest to people in the field you have chosen to study. The question preferably should arise from issues raised in the current situation, literature, or in practice. It should establish a clear purpose for the research in relation to the chosen field. For example, filling a gap in knowledge, analyzing academic assumptions or professional practice, monitoring a development in practice, comparing different approaches, or testing theories within a specific population are some of the relevant RQs.

Manageable (M): It has the similar essence as of feasibility but mainly means that the following research can be managed by the researcher.

Appropriate (A): RQ should be appropriate logically and scientifically for the community and institution.

Potential value and publishability (P): The study can make significant health impact in clinical and community practices. Therefore, research should aim for significant economic impact to reduce unnecessary or excessive costs. Furthermore, the proposed study should exist within a clinical, consumer, or policy-making context that is amenable to evidence-based change. Above all, a good RQ must address a topic that has clear implications for resolving important dilemmas in health and health-care decisions made by one or more stakeholder groups.

Systematic (S): Research is structured with specified steps to be taken in a specified sequence in accordance with the well-defined set of rules though it does not rule out creative thinking.

Example of RQ: Would the topical skin application of oil as a skin barrier reduces hypothermia in preterm infants? This question fulfills the criteria of a good RQ, that is, feasible, interesting, novel, ethical, and relevant.

Types of research question

A RQ can address different formats depending on the aspect to be evaluated.[ 6 ] For example:

• Existence: This is designed to uphold the existence of a particular phenomenon or to rule out rival explanation, for example, can neonates perceive pain?
• Description and classification: This type of question encompasses statement of uniqueness, for example, what are characteristics and types of neuropathic bladders?
• Composition: It calls for breakdown of whole into components, for example, what are stages of reflux nephropathy?
• Relationship: Evaluate relation between variables, for example, association between tumor rupture and recurrence rates in Wilm's tumor
• Descriptive—comparative: Expected that researcher will ensure that all is same between groups except issue in question, for example, Are germ cell tumors occurring in gonads more aggressive than those occurring in extragonadal sites?
• Causality: Does deletion of p53 leads to worse outcome in patients with neuroblastoma?
• Causality—comparative: Such questions frequently aim to see effect of two rival treatments, for example, does adding surgical resection improves survival rate outcome in children with neuroblastoma than with chemotherapy alone?
• Causality–Comparative interactions: Does immunotherapy leads to better survival outcome in neuroblastoma Stage IV S than with chemotherapy in the setting of adverse genetic profile than without it? (Does X cause more changes in Y than those caused by Z under certain condition and not under other conditions).

How to develop a research question

• Begin by identifying a broader subject of interest that lends itself to investigate, for example, hormone levels among hypospadias
• Do preliminary research on the general topic to find out what research has already been done and what literature already exists.[ 7 ] Therefore, one should begin with “information gaps” (What do you already know about the problem? For example, studies with results on testosterone levels among hypospadias
• What do you still need to know? (e.g., levels of other reproductive hormones among hypospadias)
• What are the implied questions: The need to know about a problem will lead to few implied questions. Each general question should lead to more specific questions (e.g., how hormone levels differ among isolated hypospadias with respect to that in normal population)
• Narrow the scope and focus of research (e.g., assessment of reproductive hormone levels among isolated hypospadias and hypospadias those with associated anomalies)
• Is RQ clear? With so much research available on any given topic, RQs must be as clear as possible in order to be effective in helping the writer direct his or her research
• Is the RQ focused? RQs must be specific enough to be well covered in the space available
• Is the RQ complex? RQs should not be answerable with a simple “yes” or “no” or by easily found facts. They should, instead, require both research and analysis on the part of the writer
• Is the RQ one that is of interest to the researcher and potentially useful to others? Is it a new issue or problem that needs to be solved or is it attempting to shed light on previously researched topic
• Is the RQ researchable? Consider the available time frame and the required resources. Is the methodology to conduct the research feasible?
• Is the RQ measurable and will the process produce data that can be supported or contradicted?
• Is the RQ too broad or too narrow?
• Create Hs: After formulating RQ, think where research is likely to be progressing? What kind of argument is likely to be made/supported? What would it mean if the research disputed the planned argument? At this step, one can well be on the way to have a focus for the research and construction of a thesis. Hs consists of more specific predictions about the nature and direction of the relationship between two variables. It is a predictive statement about the outcome of the research, dictate the method, and design of the research[ 1 ]
• Understand implications of your research: This is important for application: whether one achieves to fill gap in knowledge and how the results of the research have practical implications, for example, to develop health policies or improve educational policies.[ 1 , 8 ]

Brainstorm/Concept map for formulating research question

• First, identify what types of studies have been done in the past?
• Is there a unique area that is yet to be investigated or is there a particular question that may be worth replicating?
• Begin to narrow the topic by asking open-ended “how” and “why” questions
• Evaluate the question
• Develop a Hypothesis (Hs)
• Write down the RQ.

Writing down the research question

• State the question in your own words
• Write down the RQ as completely as possible.

For example, Evaluation of reproductive hormonal profile in children presenting with isolated hypospadias)

• Divide your question into concepts. Narrow to two or three concepts (reproductive hormonal profile, isolated hypospadias, compare with normal/not isolated hypospadias–implied)
• Specify the population to be studied (children with isolated hypospadias)
• Refer to the exposure or intervention to be investigated, if any
• Reflect the outcome of interest (hormonal profile).

Another example of a research question

Would the topical skin application of oil as a skin barrier reduces hypothermia in preterm infants? Apart from fulfilling the criteria of a good RQ, that is, feasible, interesting, novel, ethical, and relevant, it also details about the intervention done (topical skin application of oil), rationale of intervention (as a skin barrier), population to be studied (preterm infants), and outcome (reduces hypothermia).

Other important points to be heeded to while framing research question

• Make reference to a population when a relationship is expected among a certain type of subjects
• RQs and Hs should be made as specific as possible
• Avoid words or terms that do not add to the meaning of RQs and Hs
• Stick to what will be studied, not implications
• Name the variables in the order in which they occur/will be measured
• Avoid the words significant/”prove”
• Avoid using two different terms to refer to the same variable.

Some of the other problems and their possible solutions have been discussed in Table 1 .

Potential problems and solutions while making research question

G OING B EYOND F ORMULATION OF R ESEARCH Q UESTION–THE P ATH A HEAD

Once RQ is formulated, a Hs can be developed. Hs means transformation of a RQ into an operational analog.[ 1 ] It means a statement as to what prediction one makes about the phenomenon to be examined.[ 4 ] More often, for case–control trial, null Hs is generated which is later accepted or refuted.

A strong Hs should have following characteristics:

• Give insight into a RQ
• Are testable and measurable by the proposed experiments
• Have logical basis
• Follows the most likely outcome, not the exceptional outcome.

E XAMPLES OF R ESEARCH Q UESTION AND H YPOTHESIS

Research question-1.

• Does reduced gap between the two segments of the esophagus in patients of esophageal atresia reduces the mortality and morbidity of such patients?

Hypothesis-1

• Reduced gap between the two segments of the esophagus in patients of esophageal atresia reduces the mortality and morbidity of such patients
• In pediatric patients with esophageal atresia, gap of <2 cm between two segments of the esophagus and proper mobilization of proximal pouch reduces the morbidity and mortality among such patients.

Research question-2

• Does application of mitomycin C improves the outcome in patient of corrosive esophageal strictures?

Hypothesis-2

In patients aged 2–9 years with corrosive esophageal strictures, 34 applications of mitomycin C in dosage of 0.4 mg/ml for 5 min over a period of 6 months improve the outcome in terms of symptomatic and radiological relief. Some other examples of good and bad RQs have been shown in Table 2 .

Examples of few bad (left-hand side column) and few good (right-hand side) research questions

R ESEARCH Q UESTION AND S TUDY D ESIGN

RQ determines study design, for example, the question aimed to find the incidence of a disease in population will lead to conducting a survey; to find risk factors for a disease will need case–control study or a cohort study. RQ may also culminate into clinical trial.[ 9 , 10 ] For example, effect of administration of folic acid tablet in the perinatal period in decreasing incidence of neural tube defect. Accordingly, Hs is framed.

Appropriate statistical calculations are instituted to generate sample size. The subject inclusion, exclusion criteria and time frame of research are carefully defined. The detailed subject information sheet and pro forma are carefully defined. Moreover, research is set off few examples of research methodology guided by RQ:

• Incidence of anorectal malformations among adolescent females (hospital-based survey)
• Risk factors for the development of spontaneous pneumoperitoneum in pediatric patients (case–control design and cohort study)
• Effect of technique of extramucosal ureteric reimplantation without the creation of submucosal tunnel for the preservation of upper tract in bladder exstrophy (clinical trial).

The results of the research are then be available for wider applications for health and social life

C ONCLUSION

A good RQ needs thorough literature search and deep insight into the specific area/problem to be investigated. A RQ has to be focused yet simple. Research guided by such question can have wider impact in the field of social and health research by leading to formulation of policies for the benefit of larger population.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

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Natural Disaster, Tax Avoidance, and Corporate Pollution Emissions: Evidence from China

• Original Paper
• Published: 23 May 2024

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• Rui Xu 1 , 2 &
• Liuyang Ren 1  

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Our study explores how climate risk affects the tax behavior of governments and local firms, subsequently affecting corporate pollution emissions. Using data on Chinese non-state-owned industrial enterprises from 1998 to 2014, we empirically investigate the impact of natural disasters on corporate tax avoidance. The results indicate that companies in earthquake-damaged areas are less likely to avoid taxes than those in unaffected areas. Furthermore, companies that pay more taxes after a disaster can secure favorable government environmental policies, as indicated by a rise in pollution emissions. Moreover, this effect is more pronounced for less polluting firms and firms with higher financial constraints. Our study contributes to the literature on taxation and ESG from the perspective of favor-exchange in government–firm relationships.

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Do Natural Disasters Affect Corporate Tax Avoidance? The Case of Drought

The environmental cost of tax administration: evidence from a regression discontinuity design in China

Tax incentives and environmental protection: evidence from china’s taxpayer-level data, data availability.

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

The risk appears to be manifesting itself along several physical dimensions: a) earthquake risk, which can cause extensive damages in a relatively short period; b) hurricane risk, which has increased in intensity and frequency in different parts of the world; c) drought risk, occur in some particular regions; d) flood risk, affecting predominantly some regions; e) heat risk, which refers to increase in average temperatures over time.

Tax-sharing system gives Chinese local governments tax autonomy to control local corporate taxes.

See the Chinese National Earthquake Response Plan on this page https://www.gov.cn/yjgl/2012-09/21/content_ 2,230,337.htm. (Notice this page is in Chinese; Google Translate can be used to view the content.).

See Earthquake Response Plan in Huangshan City on this page https://www.huangshan.gov.cn/zwgk/public /6615714/10703207.htm. (Notice this page is in Chinese; Google Translate can be used to view the content.).

Adrian, C., Garg, M., Pham, A. V., Phang, S. Y., & Truong, C. (2023). Do natural disasters affect corporate tax avoidance? The case of drought. Journal of Business Ethics, 186 , 105–135.

Article   Google Scholar  

Armstrong, C. S., Blouin, J. L., Jagolinzer, A. D., & Larcker, D. F. (2015). Corporate governance, incentives, and tax avoidance. Journal of Accounting and Economics, 60 (1), 1–17.

Armstrong, C. S., Blouin, J. L., & Larcker, D. F. (2012). The incentives for tax planning. Journal of Accounting and Economics, 53 (1), 391–411.

Atwood, T. J., Drake, M. S., Myers, J. N., & Myers, L. A. (2012). Home country tax system characteristics and corporate tax avoidance: International evidence. The Accounting Review, 87 (6), 1831–1860.

Badertscher, B., Katz, S. P., & Rego, S. O. (2013). The separation of ownership and control and its impact on corporate tax avoidance. Journal of Accounting and Economics, 56 (2–3), 228–250.

Bai, C., Li, D. D., Tao, Z., & Wang, Y. (2000). A multitask theory of state enterprise reform. Journal of Comparative Economics, 28 , 716–738.

Bloom, N., Mahajan, A., McKenzie, D., & Roberts, J. (2010). Why do firms in developing countries have low productivity? American Economic Review, 100 (2), 619–623.

Borensztein, E., Cavallo, E., & Valenzuela, P. (2009). Debt sustainability under catastrophic risk: The case for government budget insurance. Risk Management and Insurance Review, 12 , 273–294.

Bui, B., & De Villiers, C. (2017). Business strategies and management accounting in response to climate change risk exposure and regulatory uncertainty. The British Accounting Review, 49 (1), 4–24.

Cai, H., & Liu, Q. (2009). Competition and corporate tax avoidance: Evidence from Chinese industrial firms. The Economic Journal, 119 (537), 764–795.

Calomiris, C. W., Fisman, R., & Wang, Y. (2010). Profiting from government stakes in a command economy: Evidence from Chinese asset sales. Journal of Financial Economics, 96 (3), 399–412.

Cavallo, E. (2011). Natural disasters and the economy-A survey. International Review of Environmental and Resource Economics, 5 , 63–102.

Cen, L., Maydew, E. L., Zhang, L., & Zuo, L. (2014). Customer-supplier relationships and corporate tax avoidance. Available Online at . https://doi.org/10.2139/ssrn.2442063

Chen, H., Tang, S., Wu, D., & Yang, D. (2021). The political dynamics of corporate tax avoidance: The Chinese experience. The Accounting Review, 96 (5), 157–180.

Chen, S., Chen, X., Cheng, Q., & Shevlin, T. (2010). Are family firms more tax aggressive than nonfamily firms? Journal of Financial Economics, 95 (1), 41–61.

Cheng, C. S. A., Huang, H. H., Li, Y., & Stanfield, J. (2012). The effect of hedge fund activism on corporate tax avoidance. The Accounting Review, 87 (5), 1493–1526.

Cherniwchan, J. (2017). Trade liberalization and the environment: Evidence from NAFTA and U.S. manufacturing. Journal of International Economics, 105 , 130–149. https://doi.org/10.1016/j.jinteco.2017.01.005

Chyz, J., Leung, S. C., Li, O., & Rui, O. M. (2013). Labor unions and tax aggressiveness. Journal of Financial Economics, 108 (3), 675–698.

Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of Management, 31 (6), 874–900.

Deng, Y. H., & Luo, T. (2011). Tax revenue manipulation by local taxation administrations in China. Asia-Pacific Journal of Accounting and Economics, 18 (1), 61–75.

Desai, M. A., & Dharmapala, D. (2006). Corporate tax avoidance and high-powered incentives. Social Science Electronic Publishing, 79 , 145–179.

Google Scholar  

Dessaint, O., & Matray, A. (2017). Do managers overreact to salient risks? Evidence from hurricane strikes. Journal of Financial Economics, 126 , 97–121.

Downar, B., Ernstberger, J., Reichelstein, S., Schwenen, S., & Zaklan, A. (2021). The impact of carbon disclosure mandates on emissions and financial operating performance. Review of Accounting Studies, 26 (3), 1137–1175.

Du, W., & Li, M. (2020). Assessing the impact of environmental regulation on pollution abatement and collaborative emissions reduction: Micro-evidence from Chinese industrial enterprises. Environmental Impact Assessment Review, 82 , 106382.

Dyreng, S. D., Hanlon, M., & Maydew, E. L. (2010). The effects of executives on corporate tax avoidance. The Accounting Review, 85 (4), 1163–1189.

Earnhart, D., & Lizal, L. (2006). Effects of ownership and financial performance on corporate environmental performance. Journal of Comparative Economics, 34 (1), 111–129.

Elliott, R. J. R., Strobl, E., & Sun, P. (2015). The local impact of typhoons on economic activity in China: A view from outer space. Journal of Urban Economics, 88 , 50–66.

Emerson, R. M. (1976). Social exchange theory. Annual Review of Sociology, 2 (1), 335–362.

Faccio, M., Masulis, R. W., & McConnell, J. J. (2006). Political connections and corporate bailouts. Journal of Finance, 61 , 2597–2635.

Fan, J. P., Wong, T. J., & Zhang, T. (2007). Politically connected CEOs, corporate governance, and post-IPO performance of China’s newly partially privatized firms. Journal of Financial Economics, 84 , 330–357.

Felbermayr, G., & Gröschl, J. (2014). Naturally negative: The growth effects of natural disasters. Journal of Development Economics, 111 , 92–106.

Forslid, R., Okubo, T., & Ulltveit-Moe, K. H. (2018). Why are firms that export cleaner? International trade, abatement and environmental emissions. Journal of Environmental Economics and Management, 91 , 166–183.

Goldman, E., Rocholl, J., & So, J. (2013). Politically connected boards of directors and the allocation of procurement contracts. Review of Finance, 17 , 1617–1648.

Gu, Z., Tang, S., & Wu, D. (2016). The political economy of labor cost behavior: Evidence from China. Working Paper. Retrieved from https://ssrn.com/abstract=2786533 .

Gutiérrez, E., & Teshima, K. (2018). Abatement expenditures, technology choice, and environmental performance: Evidence from firm responses to import competition in Mexico. Journal of Development Economics, 133 , 264–274.

Hadlock, C. J., & Pierce, J. R. (2010). New evidence on measuring financial constraints: Moving beyond the KZ index. Review of Financial Studies, 23 (5), 1909–1940.

Hanlon, M., & Heitzman, S. (2010a). A review of tax research. Journal of Accounting and Economics, 50 , 127–178.

Hanlon, M., & Heitzman, S. (2010b). A review of tax research. Journal of Accounting and Economics, 50 (2–3), 127–178.

Hoopes, J. L., Mescall, D., & Pittman, J. A. (2012). Do IRS audits deter corporate tax avoidance? The Accounting Review, 87 (5), 1603–1639.

Hsieh, C. T., & Klenow, P. J. (2009). Misallocation and manufacturing TFP in China and India. The Quarterly Journal of Economics, 124 (4), 1403–1448.

Imbruno, M., & Ketterer, T. D. (2018). Energy efficiency gains from importing intermediate inputs: Firm-level evidence from Indonesia. Journal of Development Economics, 135 , 117–141.

Keen, M., & Lockwood, B. (2010). The value added tax: Its causes and consequences. Journal of Development Economics, 92 (2), 138–151.

Kim, I., Wan, H., Wang, B., & Yang, T. (2019). Institutional investors and corporate environmental, social, and governance policies: Evidence from toxics release data. Management Science, 65 (10), 4901–4926.

Lei, G., Wang, W., Yu, J., & Chan, K. C. (2022). Cultural diversity and corporate tax avoidance: Evidence from Chinese private enterprises. Journal of Business Ethics, 176 , 1–23.

Leiter, A. M., Oberhofer, H., & Raschky, P. A. (2009). Creative disasters? Flooding effects on capital, labor, and productivity within European firms. Environmental and Resource Economics, 43 , 333–350.

Li, H., Meng, L., Wang, Q., & Zhou, L. A. (2008). Political connections, financing, and firm performance: Evidence from Chinese private firms. Journal of Development Economics, 87 (2), 283–299.

Li, P., Lin, Z., Du, H., Feng, T., & Zuo, J. (2021). Do environmental taxes reduce air pollution? Evidence from fossil-fuel power plants in China. Journal of Environmental Management, 295 , 113112.

Li, W., Pittman, J. A., & Wang, Z. T. (2019). The determinants and consequences of tax audits: Some evidence from China. The Journal of the American Taxation Association, 41 (1), 91–122.

Liu, S., Weng, R., & Yang, D. (2017). Natural disaster, fiscal pressure and tax avoidance: A typhoon-based study. China Journal of Accounting Studies, 5 (4), 468–509.

Liu, Z., Shen, H., Welker, M., Zhang, N., & Zhao, Y. (2021). Gone with the wind: An externality of earnings pressure. Journal of Accounting and Economics, 72 (1), 101403.

McGuire, S. T., Omer, T. C., & Wang, D. (2012). Tax avoidance: Does tax-specific industry expertise make a difference? The Accounting Review, 87 (3), 975–1003.

McGuire, S. T., Wang, D., & Wilson, R. J. (2014). Dual class ownership and tax avoidance. The Accounting Review, 89 (4), 1487–1516.

Minnick, K., & Noga, T. (2010). Do corporate governance characteristics influence tax management? Journal of Corporate Finance, 16 (5), 703–718.

Muller, A., & Whiteman, G. (2009). Exploring the geography of corporate philanthropic disaster response: A study of Fortune Global 500 firms. Journal of Business Ethics, 84 , 589–603.

Noy, I. (2009). The macroeconomic consequences of disasters. Journal of Development Economics, 88 , 221–231.

Noy, I., & Nualsri, A. (2011). Fiscal storms: Public spending and revenues in the aftermath of natural disasters. Environment and Development Economics, 16 , 113–128.

Pelling, M., Özerdem, A., & Barakat, S. (2002). The macro-economic impact of disasters. Progress in Development Studies, 2 , 283–305.

Peng, J., Xie, R., Ma, C., & Fu, Y. (2021). Market-based environmental regulation and total factor productivity: Evidence from Chinese enterprises. Economic Modelling, 95 , 394–407.

Peng, M. W., & Luo, Y. (2000). Managerial ties and firm performance in a transition economy: The nature of a micro–macro link. Academy of Management Journal, 43 (3), 486–501.

Phillips, R. (2003). Stakeholder theory and organizational ethics . Berrett-Koehler Publishers.

Raddatz, C. (2007). Are external shocks responsible for the instability of output in low-income countries? Journal of Development Economics, 84 , 155–187.

Raschky, P. A. (2008). Institutions and the losses from natural disasters. Natural Hazards & Earth System Sciences, 8 , 627–634.

Rego, S. (2003). Tax avoidance activities of U.S. multinational corporations. Contemporary Accounting Research, 20 (4), 805–833.

Rego, S. O., & Wilson, R. (2012). Equity risk incentives and corporate tax aggressiveness. Journal of Accounting Research, 50 (3), 775–810.

Robinson, J. R., Sikes, S. A., & Weaver, C. D. (2010). Performance measurement of corporate tax departments. The Accounting Review, 85 (3), 1035–1064.

Shapiro, J. S., & Walker, R. (2018). Why is pollution from US manufacturing declining? The roles of environmental regulation, productivity, and trade. American Economic Review, 108 (12), 3814–3854.

Shen, C., Jin, J., & Zou, H.-F. (2012). Fiscal decentralization in China: History, impact, challenges and next steps. Annals of Economics and Finance, 13 (1), 1–51.

Shleifer, A., & Vishny, R. (1994). Politicians and firms. The Quarterly Journal of Economics, 109 (4), 995–1025.

Song, Z., Storesletten, K., & Zilibotti, F. (2011). Growing like China. American Economic Review, 101 (1), 196–233.

Tang, T., Mo, P. L. L., & Chan, K. H. (2017). Tax collector or tax avoider? An investigation of intergovernmental agency conflicts. The Accounting Review, 92 (2), 247–270.

Toya, H., & Skidmore, M. (2007). Economic development and the impacts of natural disasters. Economics Letters, 94 , 20–25.

Vu, T. B., & Hammes, D. (2010). Dustbowls and high water, the economic impact of natural disasters in China. Asia-Pacific Journal of Social Sciences, 1 , 122–132.

Xing, Y., Liu, Y., & Cooper, S. C. L. (2018). Local government as institutional entrepreneur: Public–private collaborative partnerships in fostering regional entrepreneurship. British Journal of Management, 29 (4), 670–690.

Xu, C. (2011). The fundamental institutions of China’s reforms and development. Journal of Economic Literature, 49 (4), 1076–1151.

Yang, Z., & Shi, D. (2022). The impacts of political hierarchy on corporate pollution emissions: Evidence from a spatial discontinuity in China. Journal of Environmental Management, 302 , 113988.

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Acknowledgements

This work was supported by the National Natural Science Foundation Project of China (Grant No.72302061), the Guangdong Office of Philosophy and Social Science (Project GD23YGL21) and Philosophy and Social Science Foundation of Guangzhou (Project 2023GZGJ58). The corresponding author is Liuyang Ren. All errors remain ours. All co-authors make equal contributions to the formation of this paper.

Guangdong Office of Philosophy and Social Science, GD23YGL21, Liuyang Ren, National Natural Science Foundation of China, 72302061, Liuyang Ren, Philosophy and Social Science Foundation of Guangzhou, 2023GZGJ58, Rui Xu.

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Xu, R., Ren, L. Natural Disaster, Tax Avoidance, and Corporate Pollution Emissions: Evidence from China. J Bus Ethics (2024). https://doi.org/10.1007/s10551-024-05716-w

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• Safety-relevant features
• Features manipulations

In my ongoing column, I’ve mindfully examined other similar research studies that have earnestly sought to unlock what is happening inside generative AI. You might find of special interest this coverage at the link here and this posting at the link here . Take a look at those if you’d like to go further into the brass tacks of a fascinating and fundamental journey that is abundantly underway.

A quick comment before we leap into the fray.

Readers of my column are well aware that I eschew the ongoing misuse of wording in and around the AI arena that tries to attach human-based characteristics to today’s AI. For example, some have referred to the study that I am about to explore as having delved into the “mind” of AI or showcased the AI “brain”. Those are exasperatingly misapplied wordings. They are insidiously anthropophilic and falsely mislead people into believing that contemporary AI and humans are of the same ilk.

Please don’t fall for that type of wording.

You will hopefully observe that I try my best to avoid making use of those comparisons. I want to emphasize that we do not today have any sentient AI. Period, end of story. That might be a surprise since there is a lot of loose talk that suggests otherwise. For my detailed coverage of such matters, see the link here .

Anyway, sorry about the soapbox speech but I try to deter the rising tide of misleading characterizations whenever I get the chance to do so.

On with the show.

Trying To Get The Inner Mechanisms Figured Out

Let’s start at the beginning.

I assume you’ve used a generative AI app such as ChatGPT, GPT-4, Gemini, Bard, Claude, or the like. These are also known as large language models (LLMs) due to the aspect that they model natural languages such as English and tend to be very large-scale models that encompass a large swatch of how we use our natural languages. They are all pretty easy to use. You enter a prompt that contains your question or issue that you want solved. Upon hitting return, the AI app generates a response. You can then engage in a series of prompts and responses, acting as though you are carrying out a conversation.

Easy-peasy.

How does the generative AI app or LLM craft the responses?

In one sense, the answer is very straightforward.

The prompt that you enter is converted into a numeric format commonly referred to as tokens (see my in-depth explanation at the link here ). The numeric version of your entered words is then funneled through an elaborate maze of mathematical and computational calculations. Eventually, a response is generated, still in a numeric or tokens format, and converted back into words so that you read what it says. Voila, you then see the words displayed that were derived as a response to your entered prompt.

If we wanted to do so, it would be quite possible to follow the numbers as they weave through the mathematical and computational maze. This number would lead to that number. That number would lead to this other number. On and on this would go. It would be a rather tedious tracing of thousands upon thousands, or more like millions upon millions of numbers crisscrossing here and there.

Would a close examination of the numbers tell you what is conceptually or symbolically happening within the mathematical and computational maze?

Strictly speaking, perhaps not. It would just seem like a whole bunch of numbers. You would be hard-pressed to say anything other than that a number led to another number, and so on. Explaining how that made a difference in getting a logical or meaningful answer to your prompt would be extraordinarily difficult.

One possibility is that there isn’t any meaningful way to express the vast series of arcane calculations. Suppose that it all happens in a manner beyond our ability to understand what the underlying mathematical and computational mechanics are conceptually doing. Just be happy that it works, some might insist. We don’t need to know why, they would say.

The trouble with this is that we are increasingly finding ourselves reliant on so-called black boxes that are modern-day generative AI.

If you can’t logically or meaningfully explain how it generates responses, this ought to send chills up our spines. We have no systematic means of making sure it is doing the right thing, depending upon what is meant by doing things right. The whole concoction might go awry. It might be waylaid by evildoers, see my discussion at the link here . All manners of concern arise when we are fully dependent upon a mysterious black box that remains inscrutable to coherent explanation.

I took you through that indication to highlight that we can at least inspect the flow of numbers. One might argue that a true black box won’t let you see inside. You customarily cannot peer into a presumed black box. In the case of generative AI, it isn’t quite the proper definition of a black box. We can readily see the numbers and watch as they go back and forth.

Take a moment and mull this over.

We can watch the numbers as they proceed throughout the input-to-output processing within generative AI. We also know the data structures that are used, and we know the formulas implemented as mathematical and computational calculators. The thing we don’t know and cannot yet explain is why in a conceptual symbolic sense the outputs turn out to be strikingly fitting to the words that we input.

How can we crack open this enigma?

Much of the AI research on this beguiling topic tends to explore smaller versions of contemporary generative AI. It is a classic move of trying to get our feet wet before diving into the entire lake. The cost to play around is a lot lower on a small version of generative AI. You can also more readily observe what is happening. All in all, starting in the small is handy.

I’ve discussed the prevailing discoveries from the small-scale explorations, see the link here .

Sometimes you need to take baby steps. Begin by crawling, then standing up and stumbling, then outright walking, and hope that you’ll one day be running and sprinting. The concern raised is that what we learn from small-scale explorations might not give rise to medium-scale and large-scale explorations.

That’s a strident belief by some that size matters. If a small-sized generative AI can be mapped and explained, one viewpoint is that this doesn’t directly imply that anything larger in size can be equally explained. Perhaps there is something else that happens when the scale increases. It could be that the seemingly toy-like facets of a small-scale generative AI do not ratchet up to the big-time versions.

Okay, the gist is that with generative AI we are faced with a kind of black box that we thankfully can inspect and are presented with the issue that the large scale makes it harder and costlier to do investigations, but we can at least do our best on the smaller scale versions.

I believe you are now up-to-speed, and I can get underway with examining the recent study undertaken and posted by Anthropic.

Fasten your seat belts for an exciting ride.

Examining Generative AI At Scale

I’ll first explore an online posting entitled “Mapping the Mind of a Large Language Model” by Anthropic, posted online on May 21, 2024. There is also an accompanying online paper that I’ll get to afterward and provides deeper details. Both are worth reading.

Here are some key points from the “Mapping the Mind of a Large Language Model” posting (excerpts):

• “Today we report a significant advance in understanding the inner workings of AI models. We have identified how millions of concepts are represented inside Claude Sonnet, one of our deployed large language models. “
• “This is the first-ever detailed look inside a modern, production-grade large language model.”
• “Opening the black box doesn't necessarily help: the internal state of the model—what the model is "thinking" before writing its response—consists of a long list of numbers ("neuron activations") without a clear meaning.”
• “From interacting with a model like Claude, it's clear that it’s able to understand and wield a wide range of concepts—but we can't discern them from looking directly at neurons. It turns out that each concept is represented across many neurons, and each neuron is involved in representing many concepts.”

Allow me a moment to reflect on those points.

Before I discuss the points, I would like to say that I was saddened and disappointed at the title wording of the posting, namely “Mapping the Mind of a Large Language Model”. Can you guess why I had some heartburn?

Yes, you probably guessed that the use of the word “Mind” was lamentedly an anthropomorphic reference. I realize that in this world of seeking eyeballs, it makes for more enthralling and catchy wording. There is plenty of that these days. You will note that in one of the bullets they at least put a somewhat similar word in quotes, i.e., “thinking”, which helps somewhat to avoid an anthropomorphizing indication.

Back to the bullet points. The researchers opted to use their prior work on examining small-scale generative AI or LLM to see what they could find when using a larger-scale variant. They point out that the sea of numbers does not readily lend itself to a human-level understanding of what is meaningfully and symbolically taking place.

They mention “neurons” and such aspects as “neuron activations”.

Let me bring you into the fold.

Generative AI and LLMs tend to be designed and programmed by using mathematical and computational techniques and methods known as artificial neural networks (ANNs).

The idea for this is inspired by the human brain consisting of real neurons biochemically wired together into a complex network within our noggins. I want to loudly clarify that how artificial neural networks work is not at all akin to the true complexities of so-called wetware or the human brain, the real neurons, and the real neural networks.

Artificial neural networks are a tremendous simplification of the real things. It is at best a modicum of a computational simulation. Indeed, various aspects of artificial neural networks are not viably comparable to what happens in a real neural network. ANNs can somewhat be used to try and simulate some limited aspects of real neural networks, but at this time they are a far cry from what our brains do.

In that sense, we are once again faced with a disconcerting wording issue. When people read or hear that a computer system is using “neurons” and doing “neuron activation” they would make the reasoned leap of faith that the computer is acting exactly like our brains do. Wrong. This is more of that anthropomorphizing going on.

The dilemma for those of us in AI is that the entire field of study devoted to ANNs makes use of the same language as is used for the biological side of the neurosciences. This is certainly sensible since the inspiration for the mathematical and computational formulation is based on those facets. Plus, the hope is that someday ANNs will indeed match the real things, allowing us to fully emulate or simulate the human brain. Exciting times!

Here's what I try to do.

When I refer to ANNs and their components, I aim to use the word “artificial” in whatever related wording I use. For example, I would say “artificial neurons” when I am referring to the inspired mathematical and computational mechanisms. I would say “neurons” when referring to the biological kinds. This ends up requiring a lot of repeated uses of the word “artificial” when discussing ANNs, which some people find annoying, but I think it is worth the price to emphasize that artificial neurons are not the same today as true neurons.

You can envision that an artificial neuron is like a mathematical function that you learned in school. An artificial neuron is a mathematical function implemented computationally that takes an input and produces an output, numerically so. We can implement that mathematical function via a computer system, either as software and/or hardware (with both working hand-in-hand).

I also speak of “artificial neural activations” as those artificial neurons that upon being presented with a numeric value as an input will then perform some kind of calculation and produce an output value. The function is said to have been activated or enacted.

Not everyone abides by that convention of strictly saying “artificial” when referring to the various elements of ANNs. They assume that the reader understands that within the context of discussing generative AI and LLMs, the notion of neurons and neuron activation refers to artificial neurons and artificial neuron activation. It is a shortcut that can be confusing to some, but otherwise silently understood by those immersed in the AI field.

I’ll leave it to you to decide which convention you prefer.

Moving Further Into The Forest

Let’s next see some additional salient points indicated in the notable research study (excerpts):

• “In October 2023, we reported success applying dictionary learning to a very small "toy" language model and found coherent features corresponding to concepts like uppercase text, DNA sequences, surnames in citations, nouns in mathematics, or function arguments in Python code.” (ibid).
• “Those concepts were intriguing—but the model really was very simple.” (ibid).
• “But we were optimistic that we could scale up the technique to the vastly larger AI language models now in regular use, and in doing so, learn a great deal about the features supporting their sophisticated behaviors.” (ibid).

Those points note that the prior work had found “features” that seemed to suggest concepts exist within the morass of the artificial neural networks used in generative AI and LLMs.

Let me say something about that.

Envision that we have a whole bunch of numerical mathematical functions. Lots and lots of them. We implement them on a computer via software. We connect them such that some feed their results into others. This is our artificial neural network, and each mathematical function is considered an artificial neuron.

This is the core of our generative AI app.

We will slap on a front end that takes words via a prompt from the user and converts those words into numbers or tokens. We feed those into the artificial neural network. Numbers flow from function to function, or we would say from artificial neuron to artificial neuron. When the calculations are completed, the numeric values are fed to our front end which converts them back into readable words.

I earlier asked you whether we could make any conceptual or symbolic sense out of all those numbers flowing back and forth.

Attempts so far have usually focused on looking at clumps of artificial neurons.

Perhaps if someone asks a question about the Golden Gate Bridge, for example, there might be some clump of artificial neurons within a vast array of them that are particularly activated using that reference. Voila, we might then claim that this or that set of artificial neurons seems to represent the conceptual notion and facets pertaining to references about the Golden Gate Bridge.

In smaller-scale generative AI, this has been a mainstay of results when trying to interpret what is going on inside the generative AI. There are various sets of artificial neurons in the overall artificial neural network used within the generative AI app that seem to signify specific words or phrases. I liken this to probing a messy interconnected contrivance of Christmas lights. You might do testing and see that if you plug in this or that plug, those lights here or there light up. When you plug in a different portion, this or that lights come on.

We can do the same with generative AI. Feed in particular words. Trace what parts of the artificial neural network seem to be producing notable values, or as said to be artificial neural activations. Try this repeatedly. If you consistently observe the same clump or set being activated, you might conclude that those represent the notion of whatever word or phrase is being fed in, such as referencing the Golden Gate Bridge.

You can further test out your hypothesis by instigating things.

Suppose we removed those artificial neurons from the ANN or maybe neutralized their functions so that they were now unresponsive. Presumably, the artificial neural network might no longer be able to respond when we enter our phrase of “Golden Gate Bridge”. Or, if it does respond, it might allow us to trace to some other part of the ANN that is apparently also involved in trying to mathematically and computationally model those particular words.

I trust that you are following along on this, and it makes reasonable sense, thanks.

If we examine an artificial neural network and discover portions that seem to represent particular words or phrases, what shall we overall call that specific set or subset of artificial neurons in a generic sense?

For the sake of discussion, let’s refer to those as “features”.

A feature will be an instance of our having found what we believe to be a portion of artificial neurons that seem to demonstrably represent particular words or phrases in our artificial neural network. In a sense, you could assert that a feature represents concepts , such as the concept of what a dog is, the concept of what the Golden Gate Bridge is, and so on.

Imagine it this way. We do lots of testing and discover a clump that seems to activate when we enter the word “dog” in a prompt. Perhaps this set of artificial neurons is a mathematical and computational modeling of the concept underlying what we mean by the use of the word “dog”. We find another clump that activates whenever we enter the word “cat” in a prompt. These are each a considered feature that we’ve managed to find within the overarching artificial neural network that sits at the core of our generative AI app.

How many “features” might there be in a large-scale generative AI app?

Gosh, that’s a tough question to answer.

In theory, there could be zillions of them. There might be a so-called “feature” that represents every distinct word in the dictionary. For the English language alone, there are about 150,000 or more words in an average dictionary. Add in phases. Add in all manner of permutations and combinations of how we use words. Make sure to place the words into the context of a sentence, the context of a paragraph, and the context of an entire story or essay.

Let’s see what the referenced research study had to say:

• “We mostly treat AI models as a black box: something goes in and a response comes out, and it's not clear why the model gave that particular response instead of another.” (ibid).
• “Opening the black box doesn't necessarily help: the internal state of the model—what the model is "thinking" before writing its response—consists of a long list of numbers ("neuron activations") without a clear meaning.” (ibid).
• “Previously, we made some progress matching patterns of neuron activations, called features, to human-interpretable concepts.”
• “Just as every English word in a dictionary is made by combining letters, and every sentence is made by combining words, every feature in an AI model is made by combining neurons, and every internal state is made by combining features.”

That pretty much echoes what I said above.

Features Are Not An Island Unto Themselves

There is a vital twist noted in the above last bullet point.

Features might rely upon or be considered related to other features.

Consider this. When I use the word “dog” there are a lot of interconnected concepts that we immediately tend to think about. You might at first think of a dog as an animal with four legs. Next, you might think about types of dogs such as golden retrievers. Next, you might consider dogs you’ve known such as your beloved pet from childhood. Next, you might consider famous dogs such as Lassie. Etc.

In the AI parlance, and within the context of generative AI and LLMs, let’s say that we might find “features” that relate to other features. I would dare say we would certainly expect this to be the case. It seems unlikely that one feature upon itself could represent everything about anything of any modest complexity.

I have led you step by step to the especially exciting part of the research study (excerpts):

• “We successfully extracted millions of features from the middle layer of Claude 3.0 Sonnet, (a member of our current, state-of-the-art model family, currently available on claude.ai), providing a rough conceptual map of its internal states halfway through its computation.” (ibid).
• “Whereas the features we found in the toy language model were rather superficial, the features we found in Sonnet have a depth, breadth, and abstraction reflecting Sonnet's advanced capabilities.” (ibid).
• “A feature sensitive to mentions of the Golden Gate Bridge fires on a range of model inputs, from English mentions of the name of the bridge to discussions in Japanese, Chinese, Greek, Vietnamese, Russian, and an image.” (ibid).
• “Looking near a ‘Golden Gate Bridge’ feature, we found features for Alcatraz Island, Ghirardelli Square, the Golden State Warriors, California Governor Gavin Newsom, the 1906 earthquake, and the San Francisco-set Alfred Hitchcock film Vertigo.” (ibid).

Those are fascinating and significant results.

Here’s why.

First, it seems that the notion of “features” as used when exploring smaller-scale generative AI was useful when exploring larger-scale generative AI. That is heartwarming and quite encouraging. Were this not the case, we might have to revert to step one and start over when trying to surface the inner facets of generative AI.

Second, the features in the large-scale generative AI were said to be deeper, wider, and have a greater semblance of abstraction. This again is something we would hope to see. Small-scale generative AI cannot usually make its way out of a paper bag, while large-scale generative AI provides all the knock-your-socks fluency that we experience. The base assumption is that large-scale generative AI achieves its loftiness via having a deeper, wider, and more robust abstraction of natural language than small-scale generative AI, by far. That seems to be the case.

Third, the researchers found not just a dozen or so features, not a few hundred features, not a few thousand features, but instead, they found millions of features. Great news. If they had only found a lesser number of features, it might suggest that features are extremely hard to find or that they cloak themselves in some unknown manner.

A problem that we might face is that there could be many, many millions upon millions of features. This is a problem since we then must figure out ways to find them, track them, and figure out what we might do with them. Anytime that you have something countable in the large, this presents challenges that will require further attention.

Never a dull moment in the AI field, I can assure you of that handy-dandy rule.

Safety Is A Momentous Part Of Deciphering Generative AI

What might we want to do with the features that we uncover within generative AI?

I suppose you could stare at them and admire them. Look at what we found, might be the proud exclamation.

A perhaps more utilitarian approach would be that we could do a better job at designing and building generative AI. Knowing about features would be instrumental in boosting what we can get generative AI to accomplish. Advances in AI are bound to arise by pursuing this line of inquiry.

There is a chance too that we might learn more about the nature of language and how we use language. Keep in mind that generative AI is a massive pattern-matching mechanism. To undertake the initial data training for generative AI, usually vast swaths of the Internet are scanned, trying to pattern match how humankind makes use of words.

Maybe there are new concepts that we’ve not yet landed on in real life. Now, hidden within generative AI, and yet to be found and showcased for all to see, we might discover eye-opening concepts that no one has heretofore voiced or considered. Wow, that would be something of grand amazement.

I have so far noted the upsides of finding features.

In life, and especially in the use case of AI, there is a duality of good and bad always at play. Generative AI can be used for the good of humanity. Hooray! Generative AI can also be used in underhanded ways and be harmful to humanity. That’s the badness associated with generative AI. I cover various examples of the dual use of generative AI at the link here .

Here’s what the research study indicated on the downsides or safety considerations (excerpts):

• “Importantly, we can also manipulate these features, artificially amplifying or suppressing them to see how Claude's responses change.” (ibid).
• “We also found a feature that activates when Claude reads a scam email (this presumably supports the model’s ability to recognize such emails and warn you not to respond to them).” (ibid).
• “Normally, if one asks Claude to generate a scam email, it will refuse to do so. But when we ask the same question with the feature artificially activated sufficiently strongly, this overcomes Claude's harmlessness training and it responds by drafting a scam email.” (ibid).
• “The fact that manipulating these features causes corresponding changes to behavior validates that they aren't just correlated with the presence of concepts in input text, but also causally shape the model's behavior. In other words, the features are likely to be a faithful part of how the model internally represents the world, and how it uses these representations in its behavior.” (ibid).
• “We hope that we and others can use these discoveries to make models safer.” (ibid).

The points above note that a feature that is supposed to suppress the AI from writing scam emails could be manipulated into taking the opposite stance and proffer the most scam of scam emails that one could compose.

Your gut reaction might be that this seems mildly disconcerting, but not overly dangerous or destructive.

Let me enlarge the scope.

Suppose we make use of generative AI for the control of robots, which is already being undertaken in an initial but rapidly growing manner, see my coverage at the link here . The generative AI has been carefully data-trained to be cautious around humans and not cause any injury or harm to people.

Along comes a hacker or evildoer. They manage to examine the inner workings of the generative AI and ferret out the feature that is indicative of being careful around humans. With a few light-touch changes, they get the feature to flip around and allow harm to humans. Going even further into this diabolical scheme, the feature is altered to purposely seek to harm people.

Yikes, you might be saying.

Stop right now on all this research that is identifying features. Drop it like a lead balloon. It is going to backfire on us. These efforts are going to be a goldmine for those who have evil intentions. We are handing them a roadmap to our destruction.

You have entered into the classic debate about whether knowledge can be too much of a good thing. The AI field has been grappling with this since the beginning of AI pursuits. A counterargument is that if we hide our heads in the sand, the odds are that those evildoers are going to ferret this out anyway. By putting this into the sunshine, hopefully, we have a greater chance of devising safety capabilities that will mitigate the underhanded plots.

On a related facet, I’ve been extensively covering the field of AI ethics and AI law, which dives deeply into these momentous societal and cultural questions, see the link here and the link here , for example. You are encouraged to actively participate in determining your future and the future of those generations yet to come along.

Getting Into Overtime On The Inner Mechanisms

I promised you at the start of this discussion that we would lean into a heaping of AI terminology.

Here’s that list again:

• And more...

The first items on the list have been generally covered so far. I introduced you to the nature of generative AI, large language models, artificial neural networks, and artificial neurons. The item on the list that refers to mechanistic interpretability is the AI insider phrasing for trying to interpret the inner mechanics of what is happening within generative AI. I’ve covered that too with you.

Some of the terms toward the tail-end of the list can be readily covered straightaway.

Specifically, let’s quickly tackle these:

You know now what a feature is, and the shortlist shown here augments various feature-related aspects.

You can seemingly realize that a feature could be construed as a computational intermediary . It is a means to an end. If someone enters a prompt that says, “How do I walk my dog”, the feature within generative AI that pertains to the word “dog” is a computational intermediary that will help with mathematically and computationally assessing that portion of the sentence and aid in generating a response.

Features can be considered within various potentially identifiable features-neighborhoods. There might be a feature that represents all four-legged creatures. The feature for “dog” would likely be within that neighborhood, as would the feature for “cat”. These are collections of features, and for which a given feature might well appear in more than one neighborhood and most likely does.

The completeness of a feature entails whether the feature covers a complete aspect or only a partial aspect. For example, maybe we discover a feature associated with “dog” but this feature does not account for hairless dogs. That’s in some other feature. We might then suggest that the feature we found is incomplete.

In the terminology that lists the phrase of safety-relevant features and feature manipulations, I already mentioned that we have to be on our toes when it comes to AI safety. You are already acquainted with that phraseology.

The list is now shortened to these fanciful terms:

I’d like to take you into the full paper that the researchers provided, allowing us to unpack those pieces of terminology accordingly.

The Deepness Of The Forest Can Be Astounding

I will be quoting from the paper entitled:

• “Scaling Monosemanticity: Extracting Interpretable Features From Claude 3 Sonnet” by Adly Templeton, Tom Conerly, Jonathan Marcus, Jack Lindsey, Trenton Bricken, Brian Chen, Adam Pearce, Craig Citro, Emmanuel Ameisen, Andy Jones, Hoagy Cunningham, Nicholas L Turner, Callum McDougall, Monte MacDiarmid, Alex Tamkin, Esin Durmus, Tristan Hume, Francesco Mosconi, C. Daniel Freeman, Theodore R. Sumers, Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, and Tom Henighan, Anthropic , posted online May 21, 2024.

Let’s start with this (excerpts):

• “Our high-level goal in this work is to decompose the activations of a model (Claude 3 Sonnet) into more interpretable pieces.”
• “We do so by training a sparse autoencoder (SAE) on the model activations, as in our prior work and that of several other groups. SAEs are an instance of a family of ‘sparse dictionary learning’ algorithms that seek to decompose data into a weighted sum of sparsely active components.”
• “Our SAE consists of two layers.”
• “The first layer (‘encoder’) maps the activity to a higher-dimensional layer via a learned linear transformation followed by a ReLU nonlinearity. We refer to the units of this high-dimensional layer as “features.”
• “The second layer (‘decoder’) attempts to reconstruct the model activations via a linear transformation of the feature activations.”

That’s quite a mouthful.

I am going to explain this at a 30,000-foot level. I say that because I am going to take some liberties by simplifying what is otherwise a highly complex matter. For those trolls out there (you know who you are) that will be chagrined by the simplification, sorry about that, but if there is sufficient interest by readers, I will gladly come back around to this in a future posting and lay things out in more finite detail.

Unpacking initiated.

To try and find the features within generative AI, you could do so by hand. Go ahead and roll up those sleeves! That being said, you might as well get started immediately because to ferret out millions of them you would work by hand until the cows come home. It’s just not a practical approach when inspecting a large-scale generative AI app.

We need to devise a piece of software that will do the heavy lifting for us.

Turns out that there is a software capability known as a sparse autoencoder (SAE) that can be used for this very purpose. Thank goodness. You might find it of idle interest that an SAE is devised by using an artificial neural network. In that sense, we are going to use a tool that is based on ANN to try and ferret out the inner secrets of a large-scale ANN. Mind-bending. I discuss this further at the link here .

We can set up the SAE to examine a generative AI app when we are feeding prompts into it. Let the SAE find the various activations. This uses an underlying algorithm that is referred to as dictionary learning.

Dictionary learning essentially involves finding foundational pieces of something and then trying to build upon those toward a larger semblance, almost like examining LEGO blocks and then using those to build a structure such as a LEGO flower or LEGO house. Some AI researchers believe that dictionary learning is quite useful for this task, while others suggest that different methods might be more suitable. The jury is out on this for the moment.

Whew, go ahead and take a short break if you like, perhaps get a glass of wine. Congrats, you are halfway through this discourse on the heavy side of AI verbiage.

Let’s clock back in.

Monosemanticity is a word that frequently is used by linguists. It refers to the idea of having one meaning, wherein “mono” is of one thing and semanticity refers to the semantics of words. Some words are monosemnatic and have only one meaning, while other words are polysemantic and have more than one meaning. An example of a word that is polysemantic would be the word “bank”. If I toss the word “bank” at you and ask you what it means, you will indubitably scratch your head and probably ask me which meaning I intended. Did I mean the bank that is a financial institution, or did I mean the bank that is at the edge of a stream or river?

Features within generative AI are likely to involve some words that are monosemantic and others that are polysemantic. Usually, you can discern which meaning is coming into play by examining the associated context. When I tell you that I managed to climb up on the bank, I assume you would be thinking of a river or lake rather than your local ATM.

More Of This Complexity Enters Into The Big Picture

Let’s discuss scaling laws.

Here is a related excerpt from the cited paper:

• “Training SAEs on larger models is computationally intensive. It is important to understand (1) the extent to which additional compute improves dictionary learning results, and (2) how that compute should be allocated to obtain the highest-quality dictionary possible for a given computational budget.” (ibid).

The crux is that the running of the SAE is going to consume computer processing time. Someone has to pay for those processing cycles. We want to run the SAE as long as we can afford to do so, or at least until we believe that a desired number of features have been sufficiently found. Each feature we discover is going to cost us something in computer time used. Money, money, money.

A wise thing to do would be to try and get the most bang for our buck. No sense in having the SAE chew up valuable server time if it isn’t producing a wallop of nifty features. Scaling laws are basically rules of thumb that at some point you’ve probably done as much as you can profitably do. Going a mile more might not be especially fruitful.

This then leaves us with these last two pieces of hefty terminology to unravel:

Here are some especially relevant excerpts from the cited paper:

• “Our general approach to understanding Claude 3 Sonnet is based on the linear representation hypothesis and the superposition hypothesis.” (ibid).
• “At a high level, the linear representation hypothesis suggests that neural networks represent meaningful concepts – referred to as features – as directions in their activation spaces.” (ibid).
• “The superposition hypothesis accepts the idea of linear representations and further hypothesizes that neural networks use the existence of almost-orthogonal directions in high-dimensional spaces to represent more features than there are dimensions.” (ibid).

Tighten your belt for this.

Linear representation means that we can at times represent something of a complex nature via a somewhat simpler linear depiction. If you’ve ever taken a class in linear algebra, think about how you used various mathematical functions and numbers to represent complex graphs, spheres, and other shapes. Not only were you able to represent those elements, but you could also use numeric matrices and vectors to expand them, shrink them, rotate them, and do all manner of linear transformations.

Our hypothesis in the case of generative AI is that we can potentially adequately and sensibly represent the features within generative AI by a linear form of representation. This could be characterized as the linear representation hypothesis.

Why is it a hypothesis?

Because we might end up realizing that a linear representation won’t cut the mustard. Maybe it is insufficient for the task at hand. Perhaps we need to find some other form of representation to suitably codify and make use of features within generative AI. Right now, it seems like the right means, but we must scientifically and systematically ask ourselves whether it is fully worthy or if we need to switch to alternative means.

The superposition hypothesis is a related cousin.

I will playfully engage you in figuring out what the superposition hypothesis consists of in the context of generative AI. If you know something about physics and the role of superposition in that realm, you admittedly have a leg up on this.

Suppose you decided to watch one artificial neuron in a vast artificial neural network that sits at the core of a generative AI app. All day long, you sit there, patiently waiting for that one artificial neuron to be kicked into action. A numeric value finally flows into the artificial neuron. It does the needed calculations and then outputs a value that then flows along to another artificial neuron.

Eureka, you yell out. The artificial neuron that you had so tenderly observed was finally activated and did so when the word “dog” had been entered as part of a prompt.

Can you conclude that this one artificial neuron is solely dedicated to the facets of “dog”?

Maybe, or maybe not.

We might feed in a prompt that has the word “cat” and see this same artificial neuron be activated. There could be lots of other situations that activate this one artificial neuron. Making a brash assumption that this artificial neuron has only one singular purpose is a gutsy move. You might be right, or you might be wrong.

The world would be easier if each artificial neuron had only one purpose. Think of it this way. Once you ferreted out the purpose, you are done and never need to revisit that artificial neuron. You know what it does. Case closed.

In physics, a similar question has arisen, for example about waves. A given wave might encode multiple waves and therefore in a sense have multiple uses. A regular dictionary defines superposition as the act of having two or more things that coincide with each other.

Our use here is that it seems reasonable to believe that artificial neurons will have more than just one singular purpose. They will encode facets that will apply to more than one feature. When examining and discerning what an artificial neuron represents, we need to keep an open mind and expect that there will be multiple uses involved.

But that’s just a hypothesis, namely the superposition hypothesis.

I’m sure you know that in 1969, Astronaut Neil Armstrong stepped onto the lunar surface and uttered the immortal words “That’s one small step for man, one giant leap for mankind.”

When it comes to generative AI, the rush toward widely adopting generative AI and large language models is vast and growing in leaps and bounds. Generative AI is going to be ubiquitous. If that’s the case, we certainly ought to know what is happening inside the inner sanctum of generative AI.

A lot of small steps are still ahead of us.

Let’s aim to make a giant leap for all of humankind.

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