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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research Hypothesis 101

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

What is a hypothesis?

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

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

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

Hypothesis: sleep impacts academic performance.

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

But that’s not good enough…

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

What is a research hypothesis?

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

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

Hypothesis Essential #2: Testability (Provability)

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

For example, consider the hypothesis we mentioned earlier:

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

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

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

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

Defining A Research Hypothesis

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

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

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

What about the null hypothesis?

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

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

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

And there you have it – hypotheses in a nutshell. 

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

Psst... there’s more!

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

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16 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

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“A fact is a simple statement that everyone believes. It is innocent, unless found guilty. A hypothesis is a novel suggestion that no one wants to believe. It is guilty until found effective.”

– Edward Teller, Nuclear Physicist

During my first brainstorming meeting on my first project at McKinsey, this very serious partner, who had a PhD in Physics, looked at me and said, “So, Joe, what are your main hypotheses.” I looked back at him, perplexed, and said, “Ummm, my what?” I was used to people simply asking, “what are your best ideas, opinions, thoughts, etc.” Over time, I began to understand the importance of hypotheses and how it plays an important role in McKinsey’s problem solving of separating ideas and opinions from facts.

What is a Hypothesis?

“Hypothesis” is probably one of the top 5 words used by McKinsey consultants. And, being hypothesis-driven was required to have any success at McKinsey. A hypothesis is an idea or theory, often based on limited data, which is typically the beginning of a thread of further investigation to prove, disprove or improve the hypothesis through facts and empirical data.

The first step in being hypothesis-driven is to focus on the highest potential ideas and theories of how to solve a problem or realize an opportunity.

Let’s go over an example of being hypothesis-driven.

Let’s say you own a website, and you brainstorm ten ideas to improve web traffic, but you don’t have the budget to execute all ten ideas. The first step in being hypothesis-driven is to prioritize the ten ideas based on how much impact you hypothesize they will create.

The second step in being hypothesis-driven is to apply the scientific method to your hypotheses by creating the fact base to prove or disprove your hypothesis, which then allows you to turn your hypothesis into fact and knowledge. Running with our example, you could prove or disprove your hypothesis on the ideas you think will drive the most impact by executing:

1. An analysis of previous research and the performance of the different ideas 2. A survey where customers rank order the ideas 3. An actual test of the ten ideas to create a fact base on click-through rates and cost

While there are many other ways to validate the hypothesis on your prioritization , I find most people do not take this critical step in validating a hypothesis. Instead, they apply bad logic to many important decisions . An idea pops into their head, and then somehow it just becomes a fact.

One of my favorite lousy logic moments was a CEO who stated,

“I’ve never heard our customers talk about price, so the price doesn’t matter with our products , and I’ve decided we’re going to raise prices.”

Luckily, his management team was able to do a survey to dig deeper into the hypothesis that customers weren’t price-sensitive. Well, of course, they were and through the survey, they built a fantastic fact base that proved and disproved many other important hypotheses.

Why is being hypothesis-driven so important?

Imagine if medicine never actually used the scientific method. We would probably still be living in a world of lobotomies and bleeding people. Many organizations are still stuck in the dark ages, having built a house of cards on opinions disguised as facts, because they don’t prove or disprove their hypotheses. Decisions made on top of decisions, made on top of opinions, steer organizations clear of reality and the facts necessary to objectively evolve their strategic understanding and knowledge. I’ve seen too many leadership teams led solely by gut and opinion. The problem with intuition and gut is if you don’t ever prove or disprove if your gut is right or wrong, you’re never going to improve your intuition. There is a reason why being hypothesis-driven is the cornerstone of problem solving at McKinsey and every other top strategy consulting firm.

How do you become hypothesis-driven?

Most people are idea-driven, and constantly have hypotheses on how the world works and what they or their organization should do to improve. Though, there is often a fatal flaw in that many people turn their hypotheses into false facts, without actually finding or creating the facts to prove or disprove their hypotheses. These people aren’t hypothesis-driven; they are gut-driven.

The conversation typically goes something like “doing this discount promotion will increase our profits” or “our customers need to have this feature” or “morale is in the toilet because we don’t pay well, so we need to increase pay.” These should all be hypotheses that need the appropriate fact base, but instead, they become false facts, often leading to unintended results and consequences. In each of these cases, to become hypothesis-driven necessitates a different framing.

• Instead of “doing this discount promotion will increase our profits,” a hypothesis-driven approach is to ask “what are the best marketing ideas to increase our profits?” and then conduct a marketing experiment to see which ideas increase profits the most.

• Instead of “our customers need to have this feature,” ask the question, “what features would our customers value most?” And, then conduct a simple survey having customers rank order the features based on value to them.

• Instead of “morale is in the toilet because we don’t pay well, so we need to increase pay,” conduct a survey asking, “what is the level of morale?” what are potential issues affecting morale?” and what are the best ideas to improve morale?”

Beyond, watching out for just following your gut, here are some of the other best practices in being hypothesis-driven:

Listen to Your Intuition

Your mind has taken the collision of your experiences and everything you’ve learned over the years to create your intuition, which are those ideas that pop into your head and those hunches that come from your gut. Your intuition is your wellspring of hypotheses. So listen to your intuition, build hypotheses from it, and then prove or disprove those hypotheses, which will, in turn, improve your intuition. Intuition without feedback will over time typically evolve into poor intuition, which leads to poor judgment, thinking, and decisions.

Constantly Be Curious

I’m always curious about cause and effect. At Sports Authority, I had a hypothesis that customers that received service and assistance as they shopped, were worth more than customers who didn’t receive assistance from an associate. We figured out how to prove or disprove this hypothesis by tying surveys to transactional data of customers, and we found the hypothesis was true, which led us to a broad initiative around improving service. The key is you have to be always curious about what you think does or will drive value, create hypotheses and then prove or disprove those hypotheses.

Validate Hypotheses

You need to validate and prove or disprove hypotheses. Don’t just chalk up an idea as fact. In most cases, you’re going to have to create a fact base utilizing logic, observation, testing (see the section on Experimentation ), surveys, and analysis.

Be a Learning Organization

The foundation of learning organizations is the testing of and learning from hypotheses. I remember my first strategy internship at Mercer Management Consulting when I spent a good part of the summer combing through the results, findings, and insights of thousands of experiments that a banking client had conducted. It was fascinating to see the vastness and depth of their collective knowledge base. And, in today’s world of knowledge portals, it is so easy to disseminate, learn from, and build upon the knowledge created by companies.

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A Beginner’s Guide to Hypothesis Testing in Business

• 30 Mar 2021

Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.

If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.

Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.

Access your free e-book today.

What Is Hypothesis Testing?

To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.

A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”

Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.

Hypothesis Testing in Business

When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.

The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.

As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.

In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.

Related: 9 Fundamental Data Science Skills for Business Professionals

Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.

For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.

In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”

The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.

Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.

2. Significance Level and P-Value

Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.

With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.

In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.

3. One-Sided vs. Two-Sided Testing

When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.

Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.

4. Sampling

To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.

A survey involves asking a series of questions to a random population sample and recording self-reported responses.

Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.

Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.

Learn How to Perform Hypothesis Testing

Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.

If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.

Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .

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Why Hypotheses Beat Goals

• Developing Strategy
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Not long ago, it became fashionable to embrace failure as a sign of a company’s willingness to take risks. This trend lost favor as executives recognized that what they wanted was learning, not necessarily failure. Every failure can be attributed to a raft of missteps, and many failures do not automatically contribute to future success.

Certainly, if companies want to aggressively pursue learning, they must accept that failures will happen. But the practice of simply setting goals and then being nonchalant if they fail is inadequate.

Instead, companies should focus organizational energy on hypothesis generation and testing. Hypotheses force individuals to articulate in advance why they believe a given course of action will succeed. A failure then exposes an incorrect hypothesis — which can more reliably convert into organizational learning.

What Exactly Is a Hypothesis?

When my son was in second grade, his teacher regularly introduced topics by asking students to state some initial assumptions. For example, she introduced a unit on whales by asking: How big is a blue whale? The students all knew blue whales were big, but how big? Guesses ranged from the size of the classroom to the size of two elephants to the length of all the students in class lined up in a row. Students then set out to measure the classroom and the length of the row they formed, and they looked up the size of an elephant. They compared their results with the measurements of the whale and learned how close their estimates were.

Note that in this example, there is much more going on than just learning the size of a whale. Students were learning to recognize assumptions, make intelligent guesses based on those assumptions, determine how to test the accuracy of their guesses, and then assess the results.

This is the essence of hypothesis generation. A hypothesis emerges from a set of underlying assumptions. It is an articulation of how those assumptions are expected to play out in a given context. In short, a hypothesis is an intelligent, articulated guess that is the basis for taking action and assessing outcomes.

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Hypothesis generation in companies becomes powerful if people are forced to articulate and justify their assumptions. It makes the path from hypothesis to expected outcomes clear enough that, should the anticipated outcomes fail to materialize, people will agree that the hypothesis was faulty.

Building a culture of effective hypothesizing can lead to more thoughtful actions and a better understanding of outcomes. Not only will failures be more likely to lead to future successes, but successes will foster future successes.

Why Is Hypothesis Generation Important?

Digital technologies are creating new business opportunities, but as I’ve noted in earlier columns , companies must experiment to learn both what is possible and what customers want. Most companies are relying on empowered, agile teams to conduct these experiments. That’s because teams can rapidly hypothesize, test, and learn.

Hypothesis generation contrasts starkly with more traditional management approaches designed for process optimization. Process optimization involves telling employees both what to do and how to do it. Process optimization is fine for stable business processes that have been standardized for consistency. (Standardized processes can usually be automated, specifically because they are stable.) Increasingly, however, companies need their people to steer efforts that involve uncertainty and change. That’s when organizational learning and hypothesis generation are particularly important.

Shifting to a culture that encourages empowered teams to hypothesize isn’t easy. Established hierarchies have developed managers accustomed to directing employees on how to accomplish their objectives. Those managers invariably rose to power by being the smartest person in the room. Such managers can struggle with the requirements for leading empowered teams. They may recognize the need to hold teams accountable for outcomes rather than specific tasks, but they may not be clear about how to guide team efforts.

Some newer companies have baked this concept into their organizational structure. Leaders at the Swedish digital music service Spotify note that it is essential to provide clear missions to teams . A clear mission sets up a team to articulate measurable goals. Teams can then hypothesize how they can best accomplish those goals. The role of leaders is to quiz teams about their hypotheses and challenge their logic if those hypotheses appear to lack support.

A leader at another company told me that accountability for outcomes starts with hypotheses. If a team cannot articulate what it intends to do and what outcomes it anticipates, it is unlikely that team will deliver on its mission. In short, the success of empowered teams depends upon management shifting from directing employees to guiding their development of hypotheses. This is how leaders hold their teams accountable for outcomes.

Members of empowered teams are not the only people who need to hone their ability to hypothesize. Leaders in companies that want to seize digital opportunities are learning through their experiments which strategies hold real promise for future success. They must, in effect, hypothesize about what will make the company successful in a digital economy. If they take the next step and articulate those hypotheses and establish metrics for assessing the outcomes of their actions, they will facilitate learning about the company’s long-term success. Hypothesis generation can become a critical competency throughout a company.

How Does a Company Become Proficient at Hypothesizing?

Most business leaders have embraced the importance of evidence-based decision-making. But developing a culture of evidence-based decision-making by promoting hypothesis generation is a new challenge.

For one thing, many hypotheses are sloppy. While many people naturally hypothesize and take actions based on their hypotheses, their underlying assumptions may go unexamined. Often, they don’t clearly articulate the premise itself. The better hypotheses are straightforward and succinctly written. They’re pointed about the suppositions they’re based on. And they’re shared, allowing an audience to examine the assumptions (are they accurate?) and the postulate itself (is it an intelligent, articulated guess that is the basis for taking action and assessing outcomes?).

Related Articles

Seven-Eleven Japan offers a case in how do to hypotheses right.

For over 30 years, Seven-Eleven Japan was the most profitable retailer in Japan. It achieved that stature by relying on each store’s salesclerks to decide what items to stock on that store’s shelves. Many of the salesclerks were part-time, but they were each responsible for maximizing turnover for one part of the store’s inventory, and they received detailed reports so they could monitor their own performance.

The language of hypothesis formulation was part of their process. Each week, Seven-Eleven Japan counselors visited the stores and asked salesclerks three questions:

• What did you hypothesize this week? (That is, what did you order?)
• How did you do? (That is, did you sell what you ordered?)
• How will you do better next week? (That is, how will you incorporate the learning?)

By repeatedly asking these questions and checking the data for results, counselors helped people throughout the company hypothesize, test, and learn. The result was consistently strong inventory turnover and profitability.

How can other companies get started on this path? Evidence-based decision-making requires data — good data, as the Seven-Eleven Japan example shows. But rather than get bogged down with the limits of a company’s data, I would argue that companies can start to change their culture by constantly exposing individual hypotheses. Those hypotheses will highlight what data matters most — and the need of teams to test hypotheses will help generate enthusiasm for cleaning up bad data. A sense of accountability for generating and testing hypotheses then fosters a culture of evidence-based decision-making.

The uncertainties and speed of change in the current business environment render traditional management approaches ineffective. To create the agile, evidence-based, learning culture your business needs to succeed in a digital economy, I suggest that instead of asking What is your goal? you make it a habit to ask What is your hypothesis?

About the Author

Jeanne Ross is principal research scientist for MIT’s Center for Information Systems Research . Follow CISR on Twitter @mit_cisr .

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1 Introduction

Morgen Witzel is a Fellow of the Centre for Leadership Studies at the University of Exeter Business School. He is a writer, lecturer, and consultant on management, and is particularly well known as a writer on the history of management theory and practice. He is the author of twenty books, including Management History: Text and Cases and A History of Management Thought. He also edited the Biographical Dictionary of Management, and is a member of the editorial board of the Journal of Management History

Malcolm Warner is currently Professor and Fellow Emeritus, Wolfson College, Cambridge, and the Judge Business School, University of Cambridge. He is the author and editor of over fifty books on management, many of which have been translated, as well as of numerous articles, essays, and reviews on the subject. He has been the Editor-in-chief of the International Encyclopedia of Business and Management, vols. 1–8, London: Thomson Learning, 2nd edition, 2002. He is also Co-Editor of the Asia Pacific Business Review. He has, in addition, been a frequent Visiting Academic at many campuses and business schools across the world.

• Published: 02 April 2013
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This article introduces the book, which provides a re-evaluation of the contributions of the most significant management theorists to the field of management. It establishes the need for this volume, and presents its design and structure. The article describes the tricky process of determining who should be included in the volume, using a three-dimensional model, the three dimensions being length , breadth , and depth . There is the need to recognize the contributions that certain seminal figures, past and present, have made to the theory of management. The ‘paradigm shift’, from thinking about management to management theory and management systems, began with Frederick Taylor. Some contributions have taken many forms and range from what might be called ‘progressive orthodox’, such as those from Chester Barnard and Sumantra Ghoshal, to those of the ‘revolutionaries’ such as Taylor and Herbert Simon, to the openly heterodox such as Henry Mintzberg.

The purpose of The Oxford Handbook of Management Theorists is to produce an up-to-date evaluation—or in some cases, re-evaluation—of the contributions of the most significant management theorists to the field of management. It builds on a great deal of existing, previous work done by those interested in the history of the subject and we will adumbrate how far earlier work has shed light on the underlying narrative (see Barley and Kunda 1992 ; Warner 1998a ; Witzel 2012 ). We have attempted to cover a considerable number of management writers who have made, in our opinion, major contributions to the field. This new work is probably the largest one-volume work on management thought available in the English language and we are indebted to Oxford University Press for recruiting us as editors and commissioning this work. We hope it will be of significant use to both students and teachers of management, as well as interested practitioners.

Bringing together this collection serves several ends. First, of course, there is the need to recognize the contributions that certain seminal figures, past and present, have made to the theory of management. These contributions have taken many forms and range from what we might call ‘progressive orthodox’ such as Chester Barnard and Sumantra Ghoshal, to the ‘revolutionaries’ such as Frederick Taylor and Herbert Simon, to the openly heterodox such as Henry Mintzberg. Nor were their views developed in isolation from each other (Sibbet 1997 ). Surveys of management ideas such as Mol and Birkinshaw's ( 2008 ) and Hope and Player's ( 2012 ) demonstrate how management ideas evolve and cross-fertilize over time. In part, at least, this happens through the actions, interactions, and re-actions of key management theorists to events around them, and sometimes to each other. The contributions in this volume help to contextualize the theorists and make clear what ideas influenced them and, in turn, what influence they have had on others.

Second, there is the need to keep ideas alive. This is especially true in the case of some of the older management thinkers. As Witzel ( 2012 ) shows, it is not uncommon for good, valid theories of management to become ‘lost’, no longer studied save by a few. Even for familiar figures such as Taylor, time has a way of shrouding them in myths. The study of the modern-day reception of Taylor's work by Payne et al. ( 2006 ) shows how even the academic perception of Taylor is often at odds with reality. It is important, in our view, to understand what these seminal figures believed and why, what they offered to the world then, and what they offer now. Diversity in management thought is essential, if we are not to become too narrow in our worldview and fall into the trap described by Ghoshal ( 2005 ) of becoming too narrow and too focused, and thus at risk of the kind of disconnection from the real world described so cogently by Mintzberg. And finally, there is the need to avoid the re-invention of the wheel, or as Mol and Birkinshaw ( 2008 ) put it, the putting of old wine into new bottles. With the world of management seemingly growing ever more complex, there is a real need to look back and remember the pioneering work that has been done in this field, and then to advance from those foundations, rather than to keep reinventing management for each new generation.

Having established the need for this book, we then proceeded to its design and structure. Putting together a project like this one requires answering a number of questions from the outset. First, what is management theory ? Second, who are the management theorists ? Third, what criteria do we use for including some people in a work such as this, while excluding others? The selection process for any biographical compendium is bound to engender warm debate, and this project was no exception. Some theorists, such as Frederick Winslow Taylor or W. Edwards Deming, claimed an immediate place, thanks to the durability and impact of their ideas. Other seemingly marginal figures assumed greater importance the more we looked at them. We would certainly include C. K. Prahalad in this category, as the importance of his work has grown, and will continue to grow with the passage of time.

As to the question of what management theory is, we have deliberately taken what some might see as a narrow view. Certainly other views are possible. Lyndall Urwick ( 1933 ) believed that many of the management ideas of his day could be traced back to the seventeenth century, and modern management historians such as Daniel Wren ( 1994 ) and Morgen Witzel ( 2012 ) have shown that some management concepts have their origins in the Middle Ages or even the classical world. But while the ideas of some of these thinkers (Sun Tzu and Machiavelli, for example) became enormously influential, as Witzel ( 2012 ) points out, they do not constitute coherent bodies of theory . By the end of the nineteenth century, there was a large but largely inchoate body of ideas about management which needed to be brought together in systematic form.

The ‘paradigm shift’, from thinking about management to management theory and management systems, began with Taylor, and fittingly he is the first subject to be considered in this volume. As Robert Conti's chapter shows, Taylor was not single-handedly responsible for scientific management: others, notably the Gilbreths, played highly important roles. But he was the figure around which the first true management theory coalesced (Merkle 1980 ). And as shown too, the impact of scientific management spread across the world and persists to this day. Some later management theorists worked in Taylor's shadow, others like Mary Parker Follett reacted against him. But we would argue that all were influenced by Taylor to some extent, even if sub-consciously, or even if only to reject his ideas in favour of other, more progressive views of management.

Indeed, the cycle of acceptance and rejection is one of the features of the development of management theory over the course of the twentieth century. Paul Adler ( 2003 ) has shown the existence of a kind of ‘wave theory’ of thinking about management, in particular about organizations, moving from the scientific and mechanistic approach pioneered by Taylor and the Gilbreths to the more humanistic model of the human relations school of Follett and Mayo, then back to the more rigorous approach of management science. Herbert Simon was a key figure here, and Deming and Juran's work on quality management also fits into this model. We then move again to a more humanistic approach in the 1960s and 1970s. Peter Drucker, with his insistence that management was the last of the liberal arts, is emblematic of this trend, and Deming's later work also fits into this trend. The 1990s saw a swing back towards more mechanistic approaches, but the first decade of the twenty-first century, says Adler, saw a resumption of the trend towards humanistic theories. Following on from Adler we can see this trend manifest in the works of theorists such as Sumantra Ghoshal and C. K. Prahalad.

The tricky process of determining who should be included in this work became our next priority. The first principle was that our theorists should have produced major bodies of work on management, and made management one of their main priorities, even their sole priority. All must have believed in the importance of management and emphasized its positive role in organizations, in business, in society. Even so, the list of people who fit this category is quite long, as Warner ( 1998a ) and Witzel and his colleagues ( 2002 ) clearly demonstrate. Given that in this present work we intended to treat each subject in depth and analyse them thoroughly—in some cases, more thoroughly than has been attempted in print so far—we could only include a limited number of subjects. It became important to identify the most significant figures, and for this we needed a set of criteria.

Our response was to develop a three-dimensional model, the three dimensions being length, breadth , and depth . Figure 1.1 presents these three dimensions diagrammatically in the form of a cube.

A three-dimensional model of management

With respect to length , we may conceptualize an axis which takes in the history of the field, particularly in terms of thinking about theory. At one end, we find the earliest origins of the subject, and at the other end those ideas and theories that are the most recent. It was clear that it was important to include the earliest theorists as well as some of the more recent, so as to illustrate how management theory has changed and evolved over time. The issue of where to begin then preoccupied us. Notwithstanding the important influence and ideas of earlier thinkers such as Machiavelli, Adam Smith, and Charles Babbage (see Warner 1998a ), we decided to start with the paradigm shift mentioned earlier, the emergence in the 1890s and 1900s of complete and coherent ideas about management. This in no way downplays the importance of these earlier thinkers, but although they are key figures in terms of the dimension of length , it was difficult—in the end, impossible—to find any who matched our other criteria for breadth and depth .

Along the timeline, we chose to group our theorists into three clusters. The first of these consists of the early pioneers spanning the period from the 1890s to the 1930s. This was a critical phase in the development of management theory and we will try to unravel the complex inter-relationship and interactions between the theories—as well as theorists. Some were involved with scientific management and others with the human relations school, but others like Henri Fayol developed their ideas independently and still others, such as Lyndall Urwick, tried to synthesize the ideas of other schools. The final figure in this group is Chester Barnard, who represents a kind of transition from the pioneers to the next evolutionary phase of management thinking.

The pioneers were usually industrialists or consultants, and few had any academic background. This began to change after the Second World War. Some post-war theorists were associated with research institutes or foundations. Herbert Simon came from this background before becoming a founding faculty member at the Carnegie Institute. What is interesting about some of this group, such as Simon and Edith Penrose, is that they came from disciplines not associated with management. Rather like the pioneers, they saw the problems of management and felt that their own disciplines could be brought to bear on solving those problems; hence the uniqueness of their contributions. On the other hand, Peter Drucker's institutional associations were fairly tenuous and only gained an academic home late in the day when he went to a chair at Claremont College in California. Deming and Juran, too, were best known as consultants. As a further note, much pioneering work in this period was done by collectivities such as the Tavistock Institute, and we decided to include ‘group’ entries to encompass the whole work of these bodies, rather than just individuals within them.

The Carnegie Institute, as Augier and March ( 2011 ) have shown, became the standard bearer—or in their term, ‘poster boy’—for a revolution in business schools. Among other things, business schools increasingly became the key incubators of new research and new theory in business. Our third cluster of thinkers included nearly all stem from business school teaching and research backgrounds. Their theory and contribution is for the most part much more rigorous than that which went before, and sometimes, as in the case of Alfred Chandler or John Kotter or Henry Mintzberg, it launches a direct challenge at previously held certainties. Other modern figures, such as Ghoshal and Prahalad, looked at the changing face of the world as globalization began to emerge as an ever more powerful force, and speculated on ways that management theory could go forward. This is not to imply that such a notion was entirely absent from earlier work, but their contribution did highlight its importance.

Unlike with our first and second clusters, there is no clean break between the second and third, and no transitional figure of the stature of Barnard (although we have assigned Herbert Simon that role, perhaps tendentiously). There is considerable overlap in terms of both time and ideas. Alfred Chandler was not much younger than Herbert Simon. Yet Simon was a product of the postwar revolution, while Chandler was very definitely grounded in the ‘modern’ notion of a business school. This may seem a trivial point, but it is our observation after reading the chapters in this book that institutional roots and environment often played a very great role in the forming and shaping of ideas. Whilst we have looked at many theorists as individuals, it is clear that their contribution took place in both an institutional and societal context.

Breadth refers here to the measure of how specialist or how generalist a contribution is, the degree to which the work of a particular author is relatively narrow or wide in scope. This also may coincide with how far it is less or more technical, even complex, in its nature. A good number of contributions have clearly made their mark on the history of ‘general management’, some more and some less. It would be true to say that many of the management thinkers we have selected have been able to generalize to a degree. Similarly, some specialists who have made a very large mark on their own discipline but whose general impact on management as a whole was more limited were eliminated. Philip Kotler, despite his great impact on marketing theory and practice was omitted for this reason. This judgement may seem perverse, but again, we are concerned with theories of management, not just particular aspects of management. A further aim of this book was to treat each subject discussed as broadly as possible as well. There is more, much more, to Deming than just quality theory, or to Mintzberg than emergent strategy, or to Ghoshal than international strategy. Our purpose was to capture the thought of each of these individuals in the round.

With respect to depth , here we were concerned with the degree to which each theorist helped to alter the paradigm in the field, a much rarer quality, with minimal contribution at one end and maximal at the other. The notion of the ‘paradigm’ is taken from the field of the history and philosophy of science and associated with the work of T. S. Kuhn ( 1962 ). While it is fairly straightforward in science to identify those who have changed the paradigm, such as Newton, it is more difficult in social science and perhaps even more problematic in management (see Clarke and Clegg 2000 ). Some even talk in the plural, of paradigms, many of these sharing an anti-management quality (see Donaldson 1995 ). A strong candidate here would be Taylor (see Merkle 1980 ; Warner 1998b ; Link 2011 ). We can further argue that Simon and his colleagues at Carnegie helped change the paradigm by introducing more rigorous social scientific methods, and John Kotter helped to re-conceptualize leadership. To some extent, each of the figures or schools described here helped to change the way we think about management and added significantly to the further development of management theory. Of course none of these figures worked singlehandedly or in isolation; each had their colleagues, their forerunners, and their followers, and they need be considered as both revolutionary and evolutionary figures.

Some writers may be categorized as making a more theoretical contribution, whilst others a practical one. There might be a case for perceiving the former as having more depth than the latter. It is quite clear that a number of the seminal authors chosen contributed more to theory than others. The weight of the balance between these two considerations is brought out in the specific chapters concerned. It is said that there is more often than not an implicit theoretical dimension to practice, if there is no visible explicit one. Theory and practice are closely linked in a good number of the theorists in this volume. Much depends on the epistemology employed, as to whether the deductive or inductive approach characterizes the theorists’ work.

When we chose the seminal authors to be written about in this collection, we made a somewhat implicit intuitive choice rather than a wholly rational one, and made a judgement on the basis of ‘reputation’. Given that we are making ex post facto decisions about our inclusion, there is clearly a bias in that respect to established authors. We were able to consult with a wide range of advisors in order to make our final choice, notwithstanding our in-house academic editor. There is a rough consensus as to the centrality of many management theorists in the history of the subject, but there will be varying opinions as to the emphasis placed on the work of this one or another.

There was also an explicit criterion relating to whether they have already made their marks and whether they were ‘dead or alive’, and as will be seen, we chose a preponderance of the former. Such a view was inevitable given that we were presenting a historical narrative of the contribution of management theorists. But we also tried to balance our choice as between established Western authors and those who lived/live in other parts of the world. As well as North American and European management thinkers, we also included several from Asia; it was our intent to include still more. There was also an attempt to overcome different kinds of bias, not only relating to geographical origins, academic disciplines and so on, but also with respect to gender.

In theory, this is the methodology we used for selecting the management theorists we discuss in this volume. In practice, there have been some omissions of notable figures who could have been included under the criteria for length, breadth, and depth, for example Chris Argyris and Kenichi Ohmae. Unfortunately, authors could not be commissioned to write these chapters at the time, which we, the editors, deeply regret. It is clear from the work of the editors of this volume that they are well aware that there have been contributions to management theory from a wide range of national and societal backgrounds and origins (see Warner, 1998b ; Witzel 2012 ).

In sum, we did our best to present a selection of management thinkers that would do credit to current reputations in the present decade, as well as in the longue duree . Not everyone, however, may necessarily agree with our selection, but we hope they will be patient in reading the chapters and coming to a more considered conclusion.

Adler, P. S. ( 2003 ) ‘Toward Collaborative Interdependence: A Century of Change in the Organization of Work’, in Bruce E. Kaufman , Richard A. Beaumont , and Roy B. Helfgott (eds), Industrial Relations to Human Resources and Beyond: The Evolving Process of Employee Relations Management , Armonk, NY: M.E. Sharpe.

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Clarke, T. and Clegg, S. ( 2000 ) ‘ Management Paradigms for the New Millennium ’, International Journal of Management Reviews , 2(1): 45–64.

Donaldson, L. ( 1995 ) American Anti-Management Theories of. Organization: A Critique of Paradigm Proliferation , Cambridge: Cambridge University Press.

Ghoshal, S. ( 2005 ) ‘ Bad Management Theories are Destroying Good Management Practices ’, Academy of Management Learning and Education 4(1): 75–81.

Hope, J. and Player, S. ( 2012 ) Beyond Performance Management , Boston: Harvard Business School Press.

Kuhn, T. ( 1962 ) The Structure of Scientific Revolutions , Chicago: University of Chicago Press.

Link, S. J. (2011) ‘From Taylorism to Human Relations: American, German, and Soviet Trajectories’. Paper Presented to the Business History Conference 2011, in St. Louis.

Merkle, J. ( 1980 ) Management and Ideology: The Legacy of the International Scientific Management Movement , Berkeley: University of California Press.

Mol, M. J. and Birkinshaw, J. ( 2008 ) Giant Steps in Management: Creating Innovations That Change the Way We Work , London: FT-Prentice Hall.

Payne, S. C. , Youngcourt, S. S. , and Watrous, K. M. ( 2006 ) ‘ Portrayals of F.W. Taylor Across Textbooks ’, Journal of Management History 12(4): 385–407.

Sibbet, D. ( 1997 ) ‘ 75 Years of Management Theory and Practice ’, Harvard Business Review , Supplement, September–October.

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Warner, M. ( 1998 a) ‘Taylor, Frederick Winslow (1856–1915)’, in Warner, M. (ed.), International Encyclopedia of Business and Management , London: International Thomson Business Press.

—— ( 1998 b) The IEBM Handbook of Management Thinking , London: International Thomson Business Press.

Witzel, M. (ed.) ( 2002 ) Biographical Dictionary of Management , Bristol: Thoemmes Press.

—— ( 2012 ) A History of Management Thought , London: Routledge.

Wren, D. ( 1994 ) The Evolution of Management Thought (4th edn.), New York: John Wiley.

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Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

What is Hypothesis?

A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

Hypothesis Meaning

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

• It is made using what we already know and have seen, and it’s the basis for scientific research.
• A clear guess tells us what we think will happen in an experiment or study.
• It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
• It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Characteristics of Hypothesis

Here are some key characteristics of a hypothesis:

• Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
• Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
• Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
• Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
• Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
• Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
• Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
• Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
• Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
• Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Sources of Hypothesis

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

• Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
• Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
• Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
• Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
• Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
• Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
• Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
• Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
• Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Types of Hypothesis

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

• Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.

Non-Directional Hypothesis

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.

Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.

Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Hypothesis Examples

Following are the examples of hypotheses based on their types:

Simple Hypothesis Example

• Studying more can help you do better on tests.
• Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis Example

• How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
• A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis Example

• Drinking more sweet drinks is linked to a higher body weight score.
• Too much stress makes people less productive at work.

Non-directional Hypothesis Example

• Drinking caffeine can affect how well you sleep.
• People often like different kinds of music based on their gender.
• The average test scores of Group A and Group B are not much different.
• There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis (Ha)

• Patients on Diet A have much different cholesterol levels than those following Diet B.
• Exposure to a certain type of light can change how plants grow compared to normal sunlight.
• The average smarts score of kids in a certain school area is 100.
• The usual time it takes to finish a job using Method A is the same as with Method B.
• Having more kids go to early learning classes helps them do better in school when they get older.
• Using specific ways of talking affects how much customers get involved in marketing activities.
• Regular exercise helps to lower the chances of heart disease.
• Going to school more can help people make more money.
• Playing violent video games makes teens more likely to act aggressively.
• Less clean air directly impacts breathing health in city populations.

Functions of Hypothesis

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

How Hypothesis help in Scientific Research?

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:

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations. The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology. The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data, ultimately driving scientific progress through a cycle of testing, validation, and refinement.

FAQs on Hypothesis

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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Management Theory

• First Online: 27 April 2017

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This chapter asks the question why management theory is at all important. Why is an ability to improve—and continue to improve—managerial practice in different organizations not good enough? The answer in this chapter relates to the belief in management methods: Scholars believe that a management theory would provide a stable foundation for specific methods. Besides, theory is the crucial product of academic work.

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Scientific Management Theory Explained

Last Updated June 28, 2022

What is Scientific Management Theory?

Scientific management theory is a method of improving efficiency in the workforce. As its name implies, this management theory uses scientific methods to assess work processes.

The scientific method consists of three steps: observation, experimentation, and analysis. In science, this could mean observing the effects of a treatment, experimenting with a different treatment, and analyzing the results. Similarly, managers use scientific management theory to observe their workplaces, test different methods of completing tasks, and analyze the effect of the changes.

When properly implemented, scientific management theory improves productivity. It is an evidence-based method that prioritizes efficiency and reliability. Having scientifically rigorous work methods in place creates clear expectations for employees because it establishes a single right way to do things. It also gives managers a unified standard against which to evaluate their employees.

Scientific management theory has grown exponentially since its inception. There are now a variety of management strategies that fall under the umbrella label of scientific management theory. Each of these strategies has its own set of strengths and weaknesses. It’s important to do your own research into scientific management theory to find the best applications for it in your workplace.

The History of Scientific Management Theory

The history of scientific management theory begins with 20th century mechanical engineer Frederick Winslow Taylor. In Taylor’s time, America was on the cusp of industrialization, but management methods had not yet changed to keep up with changes in technology. While working at a steel manufacturing plant, Taylor observed several production problems. 

For one thing, there was little specialization of labor or tools. Work shifts were randomly assigned, so inexperienced workers often ended up trying and failing to complete important projects. Tools were crude, and since only a small number of tools were used for every task, they wore out quickly. For another, there was no one single “best” standard for workers to aspire to. Everyone did their job in whatever way they thought worked best, regardless of whether it was effective. Finally, managers were completely disconnected from the workers they supervised. The average manager had no idea how the workers’ tasks were performed, so they were unable to provide suggestions for improvement.

Taylor set out to solve these problems. He designed specialized shovels and other tools. He advocated for workers to be matched to the projects for which they were most naturally gifted. He trained managers in his methods so that they could implement scientific management theory in their own workplaces.

Taylor is credited with revolutionizing productivity in the American workforce. At his own steel plant, the amount of pig iron the workers could transport in a day reportedly tripled once they adopted his methods. His ideas spread rapidly and helped give rise to the Industrial Age. Scientific management is sometimes even referred to as “Taylorism” in his honor.

Taylorism and Classical Management Theory

When people talk about “Taylorism,” they often mean scientific management theory as it existed in the early 20th century. This specific management style is also called classical management theory .

Classical management theory is distinguished by three characteristics: hierarchical structure, specialization, and financial incentives. In a company operating on classical management theory, there is a rigid hierarchy. Business owners are on top, supervisors are in the middle, and regular employees are on the bottom. Everyone has a specialized, small-scale task. Anyone who is especially successful is rewarded with financial benefits.

Classic Taylorism does a good job of addressing the physical needs of workers, but it ignores social needs and creativity. Inflexible hierarchies make it difficult for talented people to rise the ranks of leadership. Specialization is efficient, but it discourages people from experimenting, and therefore prevents the development of new methods. And although good pay incentivizes good behavior, money isn’t the only thing workers care about. Employees also want to feel valued and take pride in their work.

Classical management theory is no longer widely followed, but it still has uses. Since Taylor developed his theory while working in a manufacturing plant, classic Taylorism is well-designed for manufacturers. It also tends to function better in small enterprises where everyone knows each other, and social needs are easy to address.

The Principles of Scientific Management

There are four principles of Taylorism.

• Choose methods based on science: Use the scientific method to determine the most efficient way to complete a task. Focus on increasing productivity and profits.
• Assign workers to tasks based on their natural skillset:  Get to know your workers, discover what they’re good at, and place them where their skills will be the most useful.
• Monitor your workers’ performance:  Observe what your workers are doing while they are on the clock so that you can quickly address any problems. If some workers are confused or unproductive, it is up to their managers to step in and fix the issue.
• Divide workloads appropriately between workers and managers: Make sure that managers understand how to plan and train workers and that workers understand how to implement those plans.

Goals and Objectives of Scientific Management

The primary goal of scientific management is to increase efficiency. When Taylor began his scientific management experiments, he focused on increasing efficiency by reducing the amount of time needed to perform tasks. This was a good first step, but there’s a lot more to improving efficiency than just decreasing work time. Since Taylor’s time, other innovators have found more ways to increase efficiency, such as implementing automation software.

Another objective of scientific management theory is increasing profits. If everyone is working as efficiently as possible, then they should be able to produce huge amounts of high-quality products. That translates into more sales and bigger profit margins.

Real-World Applications of Scientific Management Theory

Scientific management theory is flexible enough to be applied in just about any industry. Whether you’re designing software or selling real estate, there are certain tasks that need to be done regularly. Identifying those tasks and optimizing them for efficiency is a great way to bring Taylorism into your workplace. Here’s an example.

Imagine your company has a newsletter mailing list. Every time a new person wants to be added to the mailing list, they send an email requesting to be added. An employee then manually adds them to the list.

This is an inefficient, multi-step method of adding newsletter subscribers. Your employee probably doesn’t get any job satisfaction from typing a name into a mailing list. Moreover, the time spent manually adding names is time that could be spent on more pressing projects.

If you were the manager tasked with implementing the principles of scientific management in this company, you might suggest designing a system that automatically adds people to the mailing list as soon as they submit a request. The subscribers get newsletter access sooner and the employee now has more time to concentrate on important assignments.

Applying Scientific Management Techniques

The theory of scientific management is not perfect. Optimizing efficiency while trying to maximize profits may not solve all your workplace problems. Moreover, Taylorism has been criticized as being ineffective for modern businesses. After all, Taylor was working in a pre-industrial era. He could not have foreseen how businesses and management styles would change in the future.

Taylor’s brand of scientific management may not be a perfect fit for contemporary life. However, the scientific management theory could be a starting point for designing your own management style. You also can consider other alternative management styles such as the Great Man Theory of Leadership and the Contingency Theory of Leadership .

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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

• proposition
• supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

• counter - hypothesis
• nebular hypothesis
• null hypothesis
• planetesimal hypothesis
• Whorfian hypothesis

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This is the Difference Between a Hypothesis and a Theory

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The definition of a hypothesis in management research

The second important consideration in the formulation of a research problem in quantitative research is the construction of a hypothesis. Hypotheses bring clarity, specificity and focus to a research problem, but are not essential for a study. You can conduct a valid investigation

without constructing a single formal hypothesis. On the other hand, within the context of a research study, you can construct as many hypotheses as you consider to be appropriate. Some believe that one must formulate a hypothesis to undertake an investigation; however, the author does not hold this opinion. Hypotheses primarily arise from a set of ‘hunches’ that are tested through a study and one can conduct a perfectly valid study without having these hunches or speculations. However, in epidemiological studies, to narrow the field of investigation, it is important to formulate hypotheses.

The importance of hypotheses lies in their ability to bring direction, specificity and focus to a research study. They tell a researcher what specific information to collect, and thereby provide greater focus.

Let us imagine you are at the races and you place a bet.You bet on a hunch that a particu­lar horse will win. You will only know if your hunch was right after the race. Take another example. Suppose you have a hunch that there are more smokers than non-smokers in your class. To test your hunch, you ask either all or just some of the class if they are smokers. You can then conclude whether your hunch was right or wrong.

Now let us take a slightly different example. Suppose you work in the area of public health. Your clinical impression is that a higher rate of a particular condition prevails among people coming from a specific population subgroup. You want to find out the probable cause of this condition. There could be many causes. To explore every conceivable possibility would require an enormous amount of time and resources. Hence, to narrow the choice, based on your knowledge of the field, you could identify what you assume to be the most probable cause. You could then design a study to collect the information needed to verify your hunch. If on verification you were able to conclude that the assumed cause was the real cause of the condition, your assumption would have been right.

In these examples, you started with a superficial hunch or assumption. In one case (horse racing) you waited for the event to take place and in the other two instances you designed a study to assess the validity of your assumption, and only after careful investigation did you arrive at a conclusion about the validity of your assumptions.

Hypotheses are based upon similar logic. As a researcher you do not know about a phenom­enon, a situation, the prevalence of a condition in a population or about the outcome of a programme, but you do have a hunch to form the basis of certain assumptions or guesses. You test these, mostly one by one, by collecting information that will enable you to conclude if your hunch was right. The verification process can have one of three outcomes. Your hunch may prove to be: right, partially right or wrong. Without this process of verification, you cannot conclude anything about the validity of your assumption.

Hence, a hypothesis is a hunch, assumption, suspicion, assertion or an idea about a phenom­enon, relationship or situation, the reality or truth of which you do not know. A researcher calls these assumptions, assertions, statements or hunches hypotheses and they become the basis of an enquiry. In most studies the hypothesis will be based upon either previous studies or your own or someone else’s observations.

There are many definitions of a hypothesis. According to Kerlinger, ‘A hypothesis is a con­jectural statement of the relationship between two or more variables’ (1986: 17). Webster’s Third New International Dictionary (1976) defines a hypothesis as:

a proposition, condition, or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined.

Black and Champion define a hypothesis as ‘a tentative statement about something, the validity of which is usually unknown’ (1976: 126). In another definition, Bailey defines a hypothesis as:

a proposition that is stated in a testable form and that predicts a particular relationship between two (or more) variables. In other words, if we think that a relationship exists, we first state it as a hypothesis and then test the hypothesis in the field. (1978: 35)

According to Grinnell:

A hypothesis is written in such a way that it can be proven or disproven by valid and reliable data — it is in order to obtain these data that we perform our study. (1988: 200)

From the above definitions it is apparent that a hypothesis has certain characteristics:

• It is a tentative proposition.
• its validity is unknown.
• In most cases, it specifies a relationship between two or more variables.

Source: Kumar Ranjit (2012), Research methodology: a step-by-step guide for beginners , SAGE Publications Ltd; Third edition.

30 Jul 2021

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Management Notes

Theories of Management – 5 Major Theories Explained in Detail | Principles of Management(POM)

Theories of management.

Table of Contents

A management theory provides insight and guidance into the principles and practices of effective management. In order to make informed decisions and improve their leadership abilities, managers need to understand these management theories in detail. They have evolved over time and offer different perspectives on managing people, resources, and organizations.

The following management theories are explained in detail:

A) Scientific Management:

Developed by Frederick Taylor, Scientific Management optimizes efficiency and productivity through scientific analysis and standardization of work processes. Key points of this theory include:

Time and Motion Studies: Taylor conducted studies to break down tasks into smaller, more efficient parts. The goal of his study was to identify the best way to perform tasks so that unnecessary movements would be eliminated and productivity would increase.

Division of Labor: The division of labor principle advocates assigning specialized tasks to workers based on their skills and abilities. The division of work can increase specialization and knowledge in organizations.

Standardization: Standardization of work methods, tools, and procedures helps achieve consistency and efficiency. By establishing standard procedures, organizations reduce variability and enhance efficiency.

Incentives: Scientific Management recommends providing financial rewards or incentives to motivate workers towards meeting productivity targets. Taylor believed that employees would be motivated by a fair and consistent rewards system.

B) Administrative Management Theory:

In Administrative Management theory, Henri Fayol is associated with the overall management of organizations. Its key principles include:

Unity of Command: This principle states that every employee must receive orders from one manager in order to avoid confusion and conflicts and to ensure that clear lines of authority and accountability are maintained.

Scalar Chain: This principle emphasizes the importance of clear lines of communication and coordination through the organizational hierarchy.

Division of Work: Organizations can maximize their strengths and skills by assigning tasks according to their specializations and expertise.

Unity of Direction: This principle emphasizes the importance of aligning efforts within an organization to achieve a common goal.

C) Behavioral Management Theory:

This theory focuses on understanding and improving the behavior of individuals within organizations. Key concepts include:

Hawthorne Effect: This finding illustrates the significance of social and psychological factors in the workplace by demonstrating that employee productivity is enhanced by being aware of being observed and valued for their work.

Human Needs and Motivation: According to behavioral theorists, employees have a variety of needs and motivations that influence their behavior. According to Maslow’s Hierarchy of Needs and Herzberg’s Two-Factor Theory, fulfilling employees’ needs enhances motivation and job satisfaction.

Leadership styles: Behavioral management theory explores different leadership styles and how they influence employee performance and behavior. A manager’s attitude toward employees can be influenced by a number of different styles, including autocratic, democratic, or laissez-faire leadership.

Group Dynamics: This approach to behavioral management emphasizes the influence of group dynamics on individual behavior and performance. Employee productivity and satisfaction are strongly influenced by factors such as team building, effective communication, and conflict resolution within groups.

D) Systems Management Theory:

Systems Management views organizations as complex systems comprised of interrelated and interdependent components. It focuses on a number of key concepts, such as:

Systems Thinking: The Systems Management theory emphasizes that organizations are made up of subsystems that interact with each other and with their external environment. By using this perspective, managers gain a better understanding of how organizations interconnect and depend on one another.

Synergy: Synergy refers to the belief that an organization is greater than the sum of its parts. Systems theorists believe that subsystems can be collaboratively interconnected to create value.

Feedback loops: Using feedback loops within organizations provides valuable information from internal and external sources, allows organizations to monitor and adjust their performance, among other things.

Contingency Approach: A contingency approach acknowledges that there is no universal management approach that is suitable for every situation. When making decisions, managers need to take into account factors such as organizational culture, technology, and the external environment. Management practices should be tailored to specific circumstances and contexts.

E) Theory of Contingency Management:

It emphasizes that management practices need to be tailored to the unique circumstances and context in which they are applied. Principles of this theory include:

Fit between Strategy and Environment : A contingency approach emphasizes the importance of aligning organizational strategies with the external environment. For organizations to succeed long-term, they need to understand and adapt to their industry’s specific conditions.

Contingency Factors: Management effectiveness is influenced by a number of contingency factors. These factors include an organization’s size, industry, technology, culture, and external market conditions. Adapting management practices to these factors increases the likelihood of success.

Flexible Management Approaches: Contingency Management theory recognizes the need for flexible management approaches. There is no “one size fits all” approach to management. Managers should be able to adapt and adjust their practices according to the specific needs and circumstances of the organization.

Problem-Solving Orientation: Contingency approaches promote a problem-solving mindset among management. Instead of relying on pre-determined solutions, managers should analyze problems and make decisions based on the unique situation they face. In finding solutions, this approach encourages adaptability and creativity.

It is possible for managers to gain insight into various aspects of organizational management if they understand and apply these management theories and choose appropriate approaches depending on their unique circumstances. The theories provide frameworks for guiding decision-making, enhancing leadership abilities, and improving organizational effectiveness.

• Contingency Theory – 4 Types of Contingencies Leadership Theories | Theories of Organization
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