hypothesis definition for economics

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1.3 The Economists’ Tool Kit

Learning objectives.

• Explain how economists test hypotheses, develop economic theories, and use models in their analyses.
• Explain how the all-other-things unchanged (ceteris paribus) problem and the fallacy of false cause affect the testing of economic hypotheses and how economists try to overcome these problems.
• Distinguish between normative and positive statements.

Economics differs from other social sciences because of its emphasis on opportunity cost, the assumption of maximization in terms of one’s own self-interest, and the analysis of choices at the margin. But certainly much of the basic methodology of economics and many of its difficulties are common to every social science—indeed, to every science. This section explores the application of the scientific method to economics.

Researchers often examine relationships between variables. A variable is something whose value can change. By contrast, a constant is something whose value does not change. The speed at which a car is traveling is an example of a variable. The number of minutes in an hour is an example of a constant.

Research is generally conducted within a framework called the scientific method , a systematic set of procedures through which knowledge is created. In the scientific method, hypotheses are suggested and then tested. A hypothesis is an assertion of a relationship between two or more variables that could be proven to be false. A statement is not a hypothesis if no conceivable test could show it to be false. The statement “Plants like sunshine” is not a hypothesis; there is no way to test whether plants like sunshine or not, so it is impossible to prove the statement false. The statement “Increased solar radiation increases the rate of plant growth” is a hypothesis; experiments could be done to show the relationship between solar radiation and plant growth. If solar radiation were shown to be unrelated to plant growth or to retard plant growth, then the hypothesis would be demonstrated to be false.

If a test reveals that a particular hypothesis is false, then the hypothesis is rejected or modified. In the case of the hypothesis about solar radiation and plant growth, we would probably find that more sunlight increases plant growth over some range but that too much can actually retard plant growth. Such results would lead us to modify our hypothesis about the relationship between solar radiation and plant growth.

If the tests of a hypothesis yield results consistent with it, then further tests are conducted. A hypothesis that has not been rejected after widespread testing and that wins general acceptance is commonly called a theory . A theory that has been subjected to even more testing and that has won virtually universal acceptance becomes a law . We will examine two economic laws in the next two chapters.

Even a hypothesis that has achieved the status of a law cannot be proven true. There is always a possibility that someone may find a case that invalidates the hypothesis. That possibility means that nothing in economics, or in any other social science, or in any science, can ever be proven true. We can have great confidence in a particular proposition, but it is always a mistake to assert that it is “proven.”

Models in Economics

All scientific thought involves simplifications of reality. The real world is far too complex for the human mind—or the most powerful computer—to consider. Scientists use models instead. A model is a set of simplifying assumptions about some aspect of the real world. Models are always based on assumed conditions that are simpler than those of the real world, assumptions that are necessarily false. A model of the real world cannot be the real world.

We will encounter our first economic model in Chapter 35 “Appendix A: Graphs in Economics” . For that model, we will assume that an economy can produce only two goods. Then we will explore the model of demand and supply. One of the assumptions we will make there is that all the goods produced by firms in a particular market are identical. Of course, real economies and real markets are not that simple. Reality is never as simple as a model; one point of a model is to simplify the world to improve our understanding of it.

Economists often use graphs to represent economic models. The appendix to this chapter provides a quick, refresher course, if you think you need one, on understanding, building, and using graphs.

Models in economics also help us to generate hypotheses about the real world. In the next section, we will examine some of the problems we encounter in testing those hypotheses.

Testing Hypotheses in Economics

Here is a hypothesis suggested by the model of demand and supply: an increase in the price of gasoline will reduce the quantity of gasoline consumers demand. How might we test such a hypothesis?

Economists try to test hypotheses such as this one by observing actual behavior and using empirical (that is, real-world) data. The average retail price of gasoline in the United States rose from an average of $2.12 per gallon on May 22, 2005 to $2.88 per gallon on May 22, 2006. The number of gallons of gasoline consumed by U.S. motorists rose 0.3% during that period.

The small increase in the quantity of gasoline consumed by motorists as its price rose is inconsistent with the hypothesis that an increased price will lead to an reduction in the quantity demanded. Does that mean that we should dismiss the original hypothesis? On the contrary, we must be cautious in assessing this evidence. Several problems exist in interpreting any set of economic data. One problem is that several things may be changing at once; another is that the initial event may be unrelated to the event that follows. The next two sections examine these problems in detail.

The All-Other-Things-Unchanged Problem

The hypothesis that an increase in the price of gasoline produces a reduction in the quantity demanded by consumers carries with it the assumption that there are no other changes that might also affect consumer demand. A better statement of the hypothesis would be: An increase in the price of gasoline will reduce the quantity consumers demand, ceteris paribus. Ceteris paribus is a Latin phrase that means “all other things unchanged.”

But things changed between May 2005 and May 2006. Economic activity and incomes rose both in the United States and in many other countries, particularly China, and people with higher incomes are likely to buy more gasoline. Employment rose as well, and people with jobs use more gasoline as they drive to work. Population in the United States grew during the period. In short, many things happened during the period, all of which tended to increase the quantity of gasoline people purchased.

Our observation of the gasoline market between May 2005 and May 2006 did not offer a conclusive test of the hypothesis that an increase in the price of gasoline would lead to a reduction in the quantity demanded by consumers. Other things changed and affected gasoline consumption. Such problems are likely to affect any analysis of economic events. We cannot ask the world to stand still while we conduct experiments in economic phenomena. Economists employ a variety of statistical methods to allow them to isolate the impact of single events such as price changes, but they can never be certain that they have accurately isolated the impact of a single event in a world in which virtually everything is changing all the time.

In laboratory sciences such as chemistry and biology, it is relatively easy to conduct experiments in which only selected things change and all other factors are held constant. The economists’ laboratory is the real world; thus, economists do not generally have the luxury of conducting controlled experiments.

The Fallacy of False Cause

Hypotheses in economics typically specify a relationship in which a change in one variable causes another to change. We call the variable that responds to the change the dependent variable ; the variable that induces a change is called the independent variable . Sometimes the fact that two variables move together can suggest the false conclusion that one of the variables has acted as an independent variable that has caused the change we observe in the dependent variable.

Consider the following hypothesis: People wearing shorts cause warm weather. Certainly, we observe that more people wear shorts when the weather is warm. Presumably, though, it is the warm weather that causes people to wear shorts rather than the wearing of shorts that causes warm weather; it would be incorrect to infer from this that people cause warm weather by wearing shorts.

Reaching the incorrect conclusion that one event causes another because the two events tend to occur together is called the fallacy of false cause . The accompanying essay on baldness and heart disease suggests an example of this fallacy.

Because of the danger of the fallacy of false cause, economists use special statistical tests that are designed to determine whether changes in one thing actually do cause changes observed in another. Given the inability to perform controlled experiments, however, these tests do not always offer convincing evidence that persuades all economists that one thing does, in fact, cause changes in another.

In the case of gasoline prices and consumption between May 2005 and May 2006, there is good theoretical reason to believe the price increase should lead to a reduction in the quantity consumers demand. And economists have tested the hypothesis about price and the quantity demanded quite extensively. They have developed elaborate statistical tests aimed at ruling out problems of the fallacy of false cause. While we cannot prove that an increase in price will, ceteris paribus, lead to a reduction in the quantity consumers demand, we can have considerable confidence in the proposition.

Normative and Positive Statements

Two kinds of assertions in economics can be subjected to testing. We have already examined one, the hypothesis. Another testable assertion is a statement of fact, such as “It is raining outside” or “Microsoft is the largest producer of operating systems for personal computers in the world.” Like hypotheses, such assertions can be demonstrated to be false. Unlike hypotheses, they can also be shown to be correct. A statement of fact or a hypothesis is a positive statement .

Although people often disagree about positive statements, such disagreements can ultimately be resolved through investigation. There is another category of assertions, however, for which investigation can never resolve differences. A normative statement is one that makes a value judgment. Such a judgment is the opinion of the speaker; no one can “prove” that the statement is or is not correct. Here are some examples of normative statements in economics: “We ought to do more to help the poor.” “People in the United States should save more.” “Corporate profits are too high.” The statements are based on the values of the person who makes them. They cannot be proven false.

Because people have different values, normative statements often provoke disagreement. An economist whose values lead him or her to conclude that we should provide more help for the poor will disagree with one whose values lead to a conclusion that we should not. Because no test exists for these values, these two economists will continue to disagree, unless one persuades the other to adopt a different set of values. Many of the disagreements among economists are based on such differences in values and therefore are unlikely to be resolved.

Key Takeaways

• Economists try to employ the scientific method in their research.
• Scientists cannot prove a hypothesis to be true; they can only fail to prove it false.
• Economists, like other social scientists and scientists, use models to assist them in their analyses.
• Two problems inherent in tests of hypotheses in economics are the all-other-things-unchanged problem and the fallacy of false cause.
• Positive statements are factual and can be tested. Normative statements are value judgments that cannot be tested. Many of the disagreements among economists stem from differences in values.

Look again at the data in Table 1.1 “LSAT Scores and Undergraduate Majors” . Now consider the hypothesis: “Majoring in economics will result in a higher LSAT score.” Are the data given consistent with this hypothesis? Do the data prove that this hypothesis is correct? What fallacy might be involved in accepting the hypothesis?

Case in Point: Does Baldness Cause Heart Disease?

Mark Hunter – bald – CC BY-NC-ND 2.0.

A website called embarrassingproblems.com received the following email:

What did Dr. Margaret answer? Most importantly, she did not recommend that the questioner take drugs to treat his baldness, because doctors do not think that the baldness causes the heart disease. A more likely explanation for the association between baldness and heart disease is that both conditions are affected by an underlying factor. While noting that more research needs to be done, one hypothesis that Dr. Margaret offers is that higher testosterone levels might be triggering both the hair loss and the heart disease. The good news for people with early balding (which is really where the association with increased risk of heart disease has been observed) is that they have a signal that might lead them to be checked early on for heart disease.

Source: http://www.embarrassingproblems.com/problems/problempage230701.htm .

Answer to Try It! Problem

The data are consistent with the hypothesis, but it is never possible to prove that a hypothesis is correct. Accepting the hypothesis could involve the fallacy of false cause; students who major in economics may already have the analytical skills needed to do well on the exam.

Principles of Economics Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

1.3 How Economists Use Theories and Models to Understand Economic Issues

Learning objectives.

By the end of this section, you will be able to:

• Interpret a circular flow diagram
• Explain the importance of economic theories and models
• Describe goods and services markets and labor markets

John Maynard Keynes (1883–1946), one of the greatest economists of the twentieth century, pointed out that economics is not just a subject area but also a way of thinking. Keynes ( Figure 1.6 ) famously wrote in the introduction to a fellow economist’s book: “[Economics] is a method rather than a doctrine, an apparatus of the mind, a technique of thinking, which helps its possessor to draw correct conclusions.” In other words, economics teaches you how to think, not what to think.

Watch this video about John Maynard Keynes and his influence on economics.

Economists see the world through a different lens than anthropologists, biologists, classicists, or practitioners of any other discipline. They analyze issues and problems using economic theories that are based on particular assumptions about human behavior. These assumptions tend to be different than the assumptions an anthropologist or psychologist might use. A theory is a simplified representation of how two or more variables interact with each other. The purpose of a theory is to take a complex, real-world issue and simplify it down to its essentials. If done well, this enables the analyst to understand the issue and any problems around it. A good theory is simple enough to understand, while complex enough to capture the key features of the object or situation you are studying.

Sometimes economists use the term model instead of theory. Strictly speaking, a theory is a more abstract representation, while a model is a more applied or empirical representation. We use models to test theories, but for this course we will use the terms interchangeably.

For example, an architect who is planning a major office building will often build a physical model that sits on a tabletop to show how the entire city block will look after the new building is constructed. Companies often build models of their new products, which are more rough and unfinished than the final product, but can still demonstrate how the new product will work.

A good model to start with in economics is the circular flow diagram ( Figure 1.7 ). It pictures the economy as consisting of two groups—households and firms—that interact in two markets: the goods and services market in which firms sell and households buy and the labor market in which households sell labor to business firms or other employees.

Firms produce and sell goods and services to households in the market for goods and services (or product market). Arrow “A” indicates this. Households pay for goods and services, which becomes the revenues to firms. Arrow “B” indicates this. Arrows A and B represent the two sides of the product market. Where do households obtain the income to buy goods and services? They provide the labor and other resources (e.g., land, capital, raw materials) firms need to produce goods and services in the market for inputs (or factors of production). Arrow “C” indicates this. In return, firms pay for the inputs (or resources) they use in the form of wages and other factor payments. Arrow “D” indicates this. Arrows “C” and “D” represent the two sides of the factor market.

Of course, in the real world, there are many different markets for goods and services and markets for many different types of labor. The circular flow diagram simplifies this to make the picture easier to grasp. In the diagram, firms produce goods and services, which they sell to households in return for revenues. The outer circle shows this, and represents the two sides of the product market (for example, the market for goods and services) in which households demand and firms supply. Households sell their labor as workers to firms in return for wages, salaries, and benefits. The inner circle shows this and represents the two sides of the labor market in which households supply and firms demand.

This version of the circular flow model is stripped down to the essentials, but it has enough features to explain how the product and labor markets work in the economy. We could easily add details to this basic model if we wanted to introduce more real-world elements, like financial markets, governments, and interactions with the rest of the globe (imports and exports).

Economists carry a set of theories in their heads like a carpenter carries around a toolkit. When they see an economic issue or problem, they go through the theories they know to see if they can find one that fits. Then they use the theory to derive insights about the issue or problem. Economists express theories as diagrams, graphs, or even as mathematical equations. (Do not worry. In this course, we will mostly use graphs.) Economists do not figure out the answer to the problem first and then draw the graph to illustrate. Rather, they use the graph of the theory to help them figure out the answer. Although at the introductory level, you can sometimes figure out the right answer without applying a model, if you keep studying economics, before too long you will run into issues and problems that you will need to graph to solve. We explain both micro and macroeconomics in terms of theories and models. The most well-known theories are probably those of supply and demand, but you will learn a number of others.

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• Knowledge Base

Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

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

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

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

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

Prevent plagiarism. Run a free check.

Step 1. ask a question.

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

Step 2. Do some preliminary research

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

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

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

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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

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

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

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Definition of a Hypothesis

What it is and how it's used in sociology

• Key Concepts
• Major Sociologists
• News & Issues
• Research, Samples, and Statistics
• Recommended Reading
• Archaeology

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

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

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

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

Null Hypothesis

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

Alternative Hypothesis

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

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

Formulating a Hypothesis

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

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

Updated by Nicki Lisa Cole, Ph.D

• What It Means When a Variable Is Spurious
• Understanding Path Analysis
• Pilot Study in Research
• Simple Random Sampling
• Exploitation
• What Is Multiculturalism? Definition, Theories, and Examples
• Convenience Samples for Research
• What Is Cultural Capital? Do I Have It?
• What Does Consumerism Mean?
• Visualizing Social Stratification in the U.S.
• What Is Symbolic Interactionism?
• What Is Cultural Hegemony?
• Understanding Stratified Samples and How to Make Them
• What Is Groupthink? Definition and Examples
• What Is Ethnography?
• What Is a Reference Group?

ECON102: Principles of Macroeconomics (2021.A.01)

Economics: the study of choice, 1.3 the economists' tool kit, testing hypotheses in economics.

Here is a hypothesis suggested by the model of demand and supply: an increase in the price of gasoline will reduce the quantity of gasoline consumers demand. How might we test such a hypothesis?

Economists try to test hypotheses such as this one by observing actual behavior and using empirical (that is, real-world) data. The average retail price of gasoline in the United States rose from an average of $2.12 per gallon on May 22, 2005 to $2.88 per gallon on May 22, 2006. The number of gallons of gasoline consumed by U.S. motorists rose 0.3% during that period.

The small increase in the quantity of gasoline consumed by motorists as its price rose is inconsistent with the hypothesis that an increased price will lead to a reduction in the quantity demanded. Does that mean that we should dismiss the original hypothesis? On the contrary, we must be cautious in assessing this evidence. Several problems exist in interpreting any set of economic data. One problem is that several things may be changing at once; another is that the initial event may be unrelated to the event that follows. The next two sections examine these problems in detail.

The All-Other-Things-Unchanged Problem

The hypothesis that an increase in the price of gasoline produces a reduction in the quantity demanded by consumers carries with it the assumption that there are no other changes that might also affect consumer demand. A better statement of the hypothesis would be: An increase in the price of gasoline will reduce the quantity consumers demand, ceteris paribus. Ceteris paribus is a Latin phrase that means "all other things unchanged".

But things changed between May 2005 and May 2006. Economic activity and incomes rose both in the United States and in many other countries, particularly China, and people with higher incomes are likely to buy more gasoline. Employment rose as well, and people with jobs use more gasoline as they drive to work. Population in the United States grew during the period. In short, many things happened during the period, all of which tended to increase the quantity of gasoline people purchased.

Our observation of the gasoline market between May 2005 and May 2006 did not offer a conclusive test of the hypothesis that an increase in the price of gasoline would lead to a reduction in the quantity demanded by consumers. Other things changed and affected gasoline consumption. Such problems are likely to affect any analysis of economic events. We cannot ask the world to stand still while we conduct experiments in economic phenomena. Economists employ a variety of statistical methods to allow them to isolate the impact of single events such as price changes, but they can never be certain that they have accurately isolated the impact of a single event in a world in which virtually everything is changing all the time.

In laboratory sciences such as chemistry and biology, it is relatively easy to conduct experiments in which only selected things change and all other factors are held constant. The economists' laboratory is the real world; thus, economists do not generally have the luxury of conducting controlled experiments.

The Fallacy of False Cause

Hypotheses in economics typically specify a relationship in which a change in one variable causes another to change. We call the variable that responds to the change the dependent variable ; the variable that induces a change is called the independent variable . Sometimes the fact that two variables move together can suggest the false conclusion that one of the variables has acted as an independent variable that has caused the change we observe in the dependent variable.

Consider the following hypothesis: People wearing shorts cause warm weather. Certainly, we observe that more people wear shorts when the weather is warm. Presumably, though, it is the warm weather that causes people to wear shorts rather than the wearing of shorts that causes warm weather; it would be incorrect to infer from this that people cause warm weather by wearing shorts.

Reaching the incorrect conclusion that one event causes another because the two events tend to occur together is called the fallacy of false cause . The accompanying essay on baldness and heart disease suggests an example of this fallacy.

Because of the danger of the fallacy of false cause, economists use special statistical tests that are designed to determine whether changes in one thing actually do cause changes observed in another. Given the inability to perform controlled experiments, however, these tests do not always offer convincing evidence that persuades all economists that one thing does, in fact, cause changes in another.

In the case of gasoline prices and consumption between May 2005 and May 2006, there is good theoretical reason to believe the price increase should lead to a reduction in the quantity consumers demand. And economists have tested the hypothesis about price and the quantity demanded quite extensively. They have developed elaborate statistical tests aimed at ruling out problems of the fallacy of false cause. While we cannot prove that an increase in price will, ceteris paribus, lead to a reduction in the quantity consumers demand, we can have considerable confidence in the proposition.

Normative and Positive Statements

Two kinds of assertions in economics can be subjected to testing. We have already examined one, the hypothesis. Another testable assertion is a statement of fact, such as "It is raining outside" or "Microsoft is the largest producer of operating systems for personal computers in the world". Like hypotheses, such assertions can be demonstrated to be false. Unlike hypotheses, they can also be shown to be correct. A statement of fact or a hypothesis is a positive statement .

Although people often disagree about positive statements, such disagreements can ultimately be resolved through investigation. There is another category of assertions, however, for which investigation can never resolve differences. A normative statement is one that makes a value judgment. Such a judgment is the opinion of the speaker; no one can "prove" that the statement is or is not correct. Here are some examples of normative statements in economics: "We ought to do more to help the poor". "People in the United States should save more". "Corporate profits are too high". The statements are based on the values of the person who makes them. They cannot be proven false.

Because people have different values, normative statements often provoke disagreement. An economist whose values lead him or her to conclude that we should provide more help for the poor will disagree with one whose values lead to a conclusion that we should not. Because no test exists for these values, these two economists will continue to disagree, unless one persuades the other to adopt a different set of values. Many of the disagreements among economists are based on such differences in values and therefore are unlikely to be resolved.

Life-Cycle Hypothesis

Definition: The Life-cycle hypothesis was developed by Franco Modigliani in 1957. The theory states that individuals seek to smooth consumption over the course of a lifetime – borrowing in times of low-income and saving during periods of high income.

• As a student, it is rational to borrow to fund education.
• Then during your working life, you pay off student loans and begin saving for your retirement.
• This saving during working life enables you to maintain similar levels of income during your retirement.

It suggests wealth will build up in working age, but then fall in retirement.

Wealth in the Life-Cycle Hypothesis

The theory states consumption will be a function of wealth, expected lifetime earnings and the number of years until retirement.

Consumption will depend on

• C= consumption
• R = Years until retirement. Remaining years of work
• T= Remaining years of life

It suggests for the whole economy consumption will be a function of both wealth and income.

Prior to life-cycle theories, it was assumed that consumption was a function of income. For example, the Keynesian consumption function saw a more direct link between income and spending.

However, this failed to account for how consumption may vary depending on the position in life-cycle.

Motivation for life-cycle consumption patterns

• Diminishing marginal utility of income. If income is high during working life, there is a diminishing marginal utility of spending extra money at that particular time.
• Harder to work and earn money, in old age. Life Cycle enables people to work hard and spend less.

Does the Life-cycle theory happen in reality?

Mervyn King suggests life-cycle consumption patterns can be found in approx 75% of the population. However, 20-25% don’t plan in the long term. (NBER paper on economics of saving )

Reasons for why people don’t smooth consumption over a lifetime.

• Present focus bias – People can find it hard to value income a long time in the future
• Inertia and status quo bias . Planning for retirement requires effort, forward thinking and knowledge of financial instruments such as pensions. People may prefer to procrastinate – even though they know they should save more – and so saving gets put off.

Criticisms of Life Cycle Theory

• It assumes people run down wealth in old age, but often this doesn’t happen as people would like to pass on inherited wealth to children. Also, there can be an attachment to wealth and an unwillingness to run it down. See: Prospect theory and the endowment effect.
• It assumes people are rational and forward planning. Behavioural economics suggests many people have motivations to avoid planning.
• People may lack the self-control to reduce spending now and save more for future.
• Life-cycle is easier for people on high incomes. They are more likely to have financial knowledge, also they have the ‘luxury’ of being able to save. People on low-incomes, with high credit card debts, may feel there is no disposable income to save.
• Leisure. Rather than smoothing out consumption, individuals may prefer to smooth out leisure – working fewer hours during working age, and continuing to work part-time in retirement.
• Government means-tested benefits for old-age people may provide an incentive not to save because lower savings will lead to more social security payments.

Other theories

• Permanent income hypothesis of Milton Friedman – This states people only spend more when they see it as an increase in permanent income.
• Ricardian Equivalence  – consumers may see tax cuts as only a temporary rise in income so will not alter spending.
• Autonomous consumption – In Keynesian consumption function, the level of consumption that is independent of income.
• Marginal propensity to consume – how much of extra income is spent.

15 thoughts on “Life-Cycle Hypothesis”

Thanks for the reminder of the theory… Am a moi university Economic student in Nairobi Kenya.

Thanks for the most summarised note ever. it will help me with presentation. Gulu university. JALON

prof premraj pushpakaran writes — 2018 marks the 100th birth year of Franco Modigliani!!!

Thanks for the analysis on the hypothesis

Nice piece of work for economist. Been applicable in my presentation at Kyambogo university Uganda

This piece of paper is very important as far as consumption is concerned…

This piece is the best I have seen so far, this is a great work

Thanks for this work.it Will help me in my presentation at metropolitan international University

Very coincise and well articulated. This work reconnects me with themechanics of consumption theories. I appreaciate for a job well done.

Very nice and comprensive information. It will help me in my exams at university of jos Nigeria, studying economics

thank u for the summarized notes,it will help me in my exam at Kibabii university

Great job. Thanks for this masterpiece.

Good job. Thanks for this masterpiece. It reconnects me with the consumption theories.

A good summarised piece of work on life cycle hypothesis, it will help me in my group presentation. Kenyatta University economics student.

Comments are closed.

What is Hypothesis? Definition, Meaning, Characteristics, Sources

• Post last modified: 10 January 2022
• Reading time: 18 mins read
• Post category: Research Methodology

What is Hypothesis?

Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.

As an example, if we want to explore whether using a specific teaching method at school will result in better school marks (research question), the hypothesis could be that the mean school marks of students being taught with that specific teaching method will be higher than of those being taught using other methods.

In this example, we stated a hypothesis about the expected differences between groups. Other hypotheses may refer to correlations between variables.

Table of Content

• 1 What is Hypothesis?
• 2 Hypothesis Definition
• 3 Meaning of Hypothesis
• 4.1 Conceptual Clarity
• 4.2 Need of empirical referents
• 4.3 Hypothesis should be specific
• 4.4 Hypothesis should be within the ambit of the available research techniques
• 4.5 Hypothesis should be consistent with the theory
• 4.6 Hypothesis should be concerned with observable facts and empirical events
• 4.7 Hypothesis should be simple
• 5.1 Observation
• 5.2 Analogies
• 5.4 State of Knowledge
• 5.5 Culture
• 5.6 Continuity of Research
• 6.1 Null Hypothesis
• 6.2 Alternative Hypothesis

Thus, to formulate a hypothesis, we need to refer to the descriptive statistics (such as the mean final marks), and specify a set of conditions about these statistics (such as a difference between the means, or in a different example, a positive or negative correlation). The hypothesis we formulate applies to the population of interest.

The null hypothesis makes a statement that no difference exists (see Pyrczak, 1995, pp. 75-84).

Hypothesis Definition

A hypothesis is ‘a guess or supposition as to the existence of some fact or law which will serve to explain a connection of facts already known to exist.’ – J. E. Creighton & H. R. Smart

Hypothesis is ‘a proposition not known to be definitely true or false, examined for the sake of determining the consequences which would follow from its truth.’ – Max Black

Hypothesis is ‘a proposition which can be put to a test to determine validity and is useful for further research.’ – W. J. Goode and P. K. Hatt

A hypothesis is 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. – Webster’s New International Dictionary of the English Language (1956)

Meaning of Hypothesis

From the above mentioned definitions of hypothesis, its meaning can be explained in the following ways.

• At the primary level, a hypothesis is the possible and probable explanation of the sequence of happenings or data.
• Sometimes, hypothesis may emerge from an imagination, common sense or a sudden event.
• Hypothesis can be a probable answer to the research problem undertaken for study. 4. Hypothesis may not always be true. It can get disproven. In other words, hypothesis need not always be a true proposition.
• Hypothesis, in a sense, is an attempt to present the interrelations that exist in the available data or information.
• Hypothesis is not an individual opinion or community thought. Instead, it is a philosophical means which is to be used for research purpose. Hypothesis is not to be considered as the ultimate objective; rather it is to be taken as the means of explaining scientifically the prevailing situation.

The concept of hypothesis can further be explained with the help of some examples. Lord Keynes, in his theory of national income determination, made a hypothesis about the consumption function. He stated that the consumption expenditure of an individual or an economy as a whole is dependent on the level of income and changes in a certain proportion.

Later, this proposition was proved in the statistical research carried out by Prof. Simon Kuznets. Matthus, while studying the population, formulated a hypothesis that population increases faster than the supply of food grains. Population studies of several countries revealed that this hypothesis is true.

Validation of the Malthus’ hypothesis turned it into a theory and when it was tested in many other countries it became the famous Malthus’ Law of Population. It thus emerges that when a hypothesis is tested and proven, it becomes a theory. The theory, when found true in different times and at different places, becomes the law. Having understood the concept of hypothesis, few hypotheses can be formulated in the areas of commerce and economics.

• Population growth moderates with the rise in per capita income.
• Sales growth is positively linked with the availability of credit.
• Commerce education increases the employability of the graduate students.
• High rates of direct taxes prompt people to evade taxes.
• Good working conditions improve the productivity of employees.
• Advertising is the most effecting way of promoting sales than any other scheme.
• Higher Debt-Equity Ratio increases the probability of insolvency.
• Economic reforms in India have made the public sector banks more efficient and competent.
• Foreign direct investment in India has moved in those sectors which offer higher rate of profit.
• There is no significant association between credit rating and investment of fund.

Characteristics of Hypothesis

Not all the hypotheses are good and useful from the point of view of research. It is only a few hypotheses satisfying certain criteria that are good, useful and directive in the research work undertaken. The characteristics of such a useful hypothesis can be listed as below:

Conceptual Clarity

Need of empirical referents, hypothesis should be specific, hypothesis should be within the ambit of the available research techniques, hypothesis should be consistent with the theory, hypothesis should be concerned with observable facts and empirical events, hypothesis should be simple.

The concepts used while framing hypothesis should be crystal clear and unambiguous. Such concepts must be clearly defined so that they become lucid and acceptable to everyone. How are the newly developed concepts interrelated and how are they linked with the old one is to be very clear so that the hypothesis framed on their basis also carries the same clarity.

A hypothesis embodying unclear and ambiguous concepts can to a great extent undermine the successful completion of the research work.

A hypothesis can be useful in the research work undertaken only when it has links with some empirical referents. Hypothesis based on moral values and ideals are useless as they cannot be tested. Similarly, hypothesis containing opinions as good and bad or expectation with respect to something are not testable and therefore useless.

For example, ‘current account deficit can be lowered if people change their attitude towards gold’ is a hypothesis encompassing expectation. In case of such a hypothesis, the attitude towards gold is something which cannot clearly be described and therefore a hypothesis which embodies such an unclean thing cannot be tested and proved or disproved. In short, the hypothesis should be linked with some testable referents.

For the successful conduction of research, it is necessary that the hypothesis is specific and presented in a precise manner. Hypothesis which is general, too ambitious and grandiose in scope is not to be made as such hypothesis cannot be easily put to test. A hypothesis is to be based on such concepts which are precise and empirical in nature. A hypothesis should give a clear idea about the indicators which are to be used.

For example, a hypothesis that economic power is increasingly getting concentrated in a few hands in India should enable us to define the concept of economic power. It should be explicated in terms of measurable indicator like income, wealth, etc. Such specificity in the formulation of a hypothesis ensures that the research is practicable and significant.

While framing the hypothesis, the researcher should be aware of the available research techniques and should see that the hypothesis framed is testable on the basis of them. In other words, a hypothesis should be researchable and for this it is important that a due thought has been given to the methods and techniques which can be used to measure the concepts and variables embodied in the hypothesis.

It does not however mean that hypotheses which are not testable with the available techniques of research are not to be made. If the problem is too significant and therefore the hypothesis framed becomes too ambitious and complex, it’s testing becomes possible with the development of new research techniques or the hypothesis itself leads to the development of new research techniques.

A hypothesis must be related to the existing theory or should have a theoretical orientation. The growth of knowledge takes place in the sequence of facts, hypothesis, theory and law or principles. It means the hypothesis should have a correspondence with the existing facts and theory.

If the hypothesis is related to some theory, the research work will enable us to support, modify or refute the existing theory. Theoretical orientation of the hypothesis ensures that it becomes scientifically useful. According to Prof. Goode and Prof. Hatt, research work can contribute to the existing knowledge only when the hypothesis is related with some theory.

This enables us to explain the observed facts and situations and also verify the framed hypothesis. In the words of Prof. Cohen and Prof. Nagel, “hypothesis must be formulated in such a manner that deduction can be made from it and that consequently a decision can be reached as to whether it does or does not explain the facts considered.”

If the research work based on a hypothesis is to be successful, it is necessary that the later is as simple and easy as possible. An ambition of finding out something new may lead the researcher to frame an unrealistic and unclear hypothesis. Such a temptation is to be avoided. Framing a simple, easy and testable hypothesis requires that the researcher is well acquainted with the related concepts.

Sources of Hypothesis

Hypotheses can be derived from various sources. Some of the sources is given below:

Observation

State of knowledge, continuity of research.

Hypotheses can be derived from observation from the observation of price behavior in a market. For example the relationship between the price and demand for an article is hypothesized.

Analogies are another source of useful hypotheses. Julian Huxley has pointed out that casual observations in nature or in the framework of another science may be a fertile source of hypotheses. For example, the hypotheses that similar human types or activities may be found in similar geophysical regions come from plant ecology.

This is one of the main sources of hypotheses. It gives direction to research by stating what is known logical deduction from theory lead to new hypotheses. For example, profit / wealth maximization is considered as the goal of private enterprises. From this assumption various hypotheses are derived’.

An important source of hypotheses is the state of knowledge in any particular science where formal theories exist hypotheses can be deduced. If the hypotheses are rejected theories are scarce hypotheses are generated from conception frameworks.

Another source of hypotheses is the culture on which the researcher was nurtured. Western culture has induced the emergence of sociology as an academic discipline over the past decade, a large part of the hypotheses on American society examined by researchers were connected with violence. This interest is related to the considerable increase in the level of violence in America.

The continuity of research in a field itself constitutes an important source of hypotheses. The rejection of some hypotheses leads to the formulation of new ones capable of explaining dependent variables in subsequent research on the same subject.

Null and Alternative Hypothesis

Null hypothesis.

The hypothesis that are proposed with the intent of receiving a rejection for them are called Null Hypothesis . This requires that we hypothesize the opposite of what is desired to be proved. For example, if we want to show that sales and advertisement expenditure are related, we formulate the null hypothesis that they are not related.

Similarly, if we want to conclude that the new sales training programme is effective, we formulate the null hypothesis that the new training programme is not effective, and if we want to prove that the average wages of skilled workers in town 1 is greater than that of town 2, we formulate the null hypotheses that there is no difference in the average wages of the skilled workers in both the towns.

Since we hypothesize that sales and advertisement are not related, new training programme is not effective and the average wages of skilled workers in both the towns are equal, we call such hypotheses null hypotheses and denote them as H 0 .

Alternative Hypothesis

Rejection of null hypotheses leads to the acceptance of alternative hypothesis . The rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement expenditure) or the difference between means (e.g., wages of skilled workers in town 1 and town 2) or the difference between proportions have statistical significance and the acceptance of the null hypotheses indicates that these differences are due to chance.

As already mentioned, the alternative hypotheses specify that values/relation which the researcher believes hold true. The alternative hypotheses can cover a whole range of values rather than a single point. The alternative hypotheses are denoted by H 1 .

Business Ethics

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Corporate social responsibility (CSR)

• Theories of CSR
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Lean Six Sigma

• What is Six Sigma?
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• Value and Waste in Lean Six Sigma
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• What is Binomial, Poisson, Normal Distribution?
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• What is Process Audits?
• Six Sigma Implementation at Ford
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• Research Methodology
• What is Research?
• Sampling Method
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• Data Collection in Research
• Methods of Collecting Data
• Application of Business Research
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Research Report

• What is Management?
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Service Operations Management

• What is Service?
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• What is E-Procurement?
• E-marketplace and Online Catalogues
• Fixed Price and Cost Reimbursement Contracts
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Strategic Management

• What is Strategic Management?
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• Prahalad and Gary Hammel
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• What is Technology Life Cycle?
• What is Diversification Strategy?
• What is Corporate Restructuring Strategy?
• Resources and Capabilities of Organization
• Role of Leaders In Functional-Level Strategic Management
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Supply Chain

• What is Supply Chain Management?
• Supply Chain Planning and Measuring Strategy Performance
• What is Warehousing?
• What is Packaging?
• What is Inventory Management?
• What is Material Handling?
• What is Order Picking?
• Receiving and Dispatch, Processes
• What is Warehouse Design?
• What is Warehousing Costs?

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• Education Resources Information Center - Understanding Hypotheses, Predictions, Laws, and Theories
• Simply Psychology - Research Hypothesis: Definition, Types, & Examples
• Cornell University - The Learning Strategies Center - Hypothesis
• Washington State University - Developing a Hypothesis
• Verywell Mind - Forming a Good Hypothesis for Scientific Research
• BCCampus Publishing - Research Methods for the Social Sciences: An Introduction - Hypotheses

hypothesis , something supposed or taken for granted, with the object of following out its consequences (Greek hypothesis , “a putting under,” the Latin equivalent being suppositio ).

In planning a course of action, one may consider various alternatives , working out each in detail. Although the word hypothesis is not typically used in this case, the procedure is virtually the same as that of an investigator of crime considering various suspects. Different methods may be used for deciding what the various alternatives may be, but what is fundamental is the consideration of a supposal as if it were true, without actually accepting it as true. One of the earliest uses of the word in this sense was in geometry . It is described by Plato in the Meno .

The most important modern use of a hypothesis is in relation to scientific investigation . A scientist is not merely concerned to accumulate such facts as can be discovered by observation: linkages must be discovered to connect those facts. An initial puzzle or problem provides the impetus , but clues must be used to ascertain which facts will help yield a solution. The best guide is a tentative hypothesis, which fits within the existing body of doctrine. It is so framed that, with its help, deductions can be made that under certain factual conditions (“initial conditions”) certain other facts would be found if the hypothesis were correct.

The concepts involved in the hypothesis need not themselves refer to observable objects. However, the initial conditions should be able to be observed or to be produced experimentally, and the deduced facts should be able to be observed. William Harvey ’s research on circulation in animals demonstrates how greatly experimental observation can be helped by a fruitful hypothesis. While a hypothesis can be partially confirmed by showing that what is deduced from it with certain initial conditions is actually found under those conditions, it cannot be completely proved in this way. What would have to be shown is that no other hypothesis would serve. Hence, in assessing the soundness of a hypothesis, stress is laid on the range and variety of facts that can be brought under its scope. Again, it is important that it should be capable of being linked systematically with hypotheses which have been found fertile in other fields.

If the predictions derived from the hypothesis are not found to be true, the hypothesis may have to be given up or modified. The fault may lie, however, in some other principle forming part of the body of accepted doctrine which has been utilized in deducing consequences from the hypothesis. It may also lie in the fact that other conditions, hitherto unobserved, are present beside the initial conditions, affecting the result. Thus the hypothesis may be kept, pending further examination of facts or some remodeling of principles. A good illustration of this is to be found in the history of the corpuscular and the undulatory hypotheses about light .

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Efficient Market Hypothesis (EMH) Tenets and Variations

Problems of emh, qualifying the emh, increasing market efficiency, the bottom line, efficient market hypothesis: is the stock market efficient.

An important debate among investors is whether the stock market is efficient—that is, whether it reflects all the information made available to market participants at any given time. The efficient market hypothesis (EMH) maintains that all stocks are perfectly priced according to their inherent investment properties, the knowledge of which all market participants possess equally.

Financial theories are subjective. In other words, there are no proven laws in finance. Instead, ideas try to explain how the market works. Here, we take a look at where the efficient market hypothesis has fallen short in terms of explaining the stock market's behavior. While it may be easy to see a number of deficiencies in the theory, it's important to explore its relevance in the modern investing environment.

Key Takeaways

• The Efficient Market Hypothesis assumes all stocks trade at their fair value.
• The weak tenet implies stock prices reflect all available information, the semi-strong implies stock prices are factored into all publicly available information, and the strong tenet implies all information is already factored into the stock prices.
• The theory assumes it would be impossible to outperform the market and that all investors interpret available information the same way.
• Although most decisions are still made by humans, the use of computers to analyze information may be making the theory more relevant.

There are three tenets to the efficient market hypothesis: the weak, the semi-strong, and the strong.

The weak make the assumption that current stock prices reflect all available information. It goes further to say past performance is irrelevant to what the future holds for the stock. Therefore, it assumes that technical analysis can't be used to achieve returns.

The semi-strong form of the theory contends stock prices are factored into all information that is publicly available. Therefore, investors can't use fundamental analysis to beat the market and make significant gains.

In the strong form of the theory, all information—both public and private—are already factored into the stock prices. So it assumes no one has an advantage to the information available, whether that's someone on the inside or out. Therefore, it implies the market is perfect, and making excessive profits from the market is next to impossible.

The EMH was developed from economist Eugene Fama's Ph.D. dissertation in the 1960s.

While it may sound great, this theory is not without criticism. Other schools of thought, such as Alphanomics , argue that markets can be inefficient.

First, the efficient market hypothesis assumes all investors perceive all available information in precisely the same manner. The different methods for analyzing and valuing stocks pose some problems for the validity of the EMH. If one investor looks for undervalued market opportunities while another evaluates a stock on the basis of its growth potential, these two investors will already have arrived at a different assessment of the stock's fair market value . Therefore, one argument against the EMH points out that since investors value stocks differently, it is impossible to determine what a stock should be worth in an efficient market.

Proponents of the EMH conclude investors may profit from investing in a low-cost, passive portfolio.

Secondly, no single investor is ever able to attain greater profitability than another with the same amount of invested funds under the efficient market hypothesis. Since they both have the same information, they can only achieve identical returns. But consider the wide range of investment returns attained by the entire universe of investors, investment funds , and so forth. If no investor had any clear advantage over another, would there be a range of yearly returns in the mutual fund industry, from significant losses to 50% profits or more? According to the EMH, if one investor is profitable, it means every investor is profitable. But this is far from true.

Thirdly (and closely related to the second point), under the efficient market hypothesis, no investor should ever be able to beat the market or the average annual returns that all investors and funds are able to achieve using their best efforts. This would naturally imply, as many market experts often maintain, the absolute best investment strategy is simply to place all of one's investment funds into an index fund. This would increase or decrease according to the overall level of corporate profitability or losses. But there are many investors who have consistently beaten the market. Warren Buffett is one of those who's managed to outpace the averages year after year.

Eugene Fama never imagined that his efficient market would be 100% efficient all the time. That would be impossible, as it takes time for stock prices to respond to new information. The efficient hypothesis, however, doesn't give a strict definition of how much time prices need to revert to fair value . Moreover, under an efficient market, random events are entirely acceptable, but will always be ironed out as prices revert to the norm.

But it's important to ask whether EMH undermines itself by allowing random occurrences or environmental eventualities. There is no doubt that such eventualities must be considered under market efficiency but, by definition, true efficiency accounts for those factors immediately. In other words, prices should respond nearly instantaneously with the release of new information that can be expected to affect a stock's investment characteristics. So, if the EMH allows for inefficiencies, it may have to admit that absolute market efficiency is impossible.

Although it's relatively easy to pour cold water on the efficient market hypothesis, its relevance may actually be growing. With the rise of computerized systems to analyze stock investments, trades, and corporations, investments are becoming increasingly automated on the basis of strict mathematical or fundamental analytical methods. Given the right power and speed, some computers can immediately process any and all available information, and even translate such analysis into an immediate trade execution.

Despite the increasing use of computers, most decision-making is still done by human beings and is therefore subject to human error. Even at an institutional level, the use of analytical machines is anything but universal. While the success of stock market investing is based mostly on the skill of individual or institutional investors, people will continually search for the surefire method of achieving greater returns than the market averages.

It's safe to say the market is not going to achieve perfect efficiency anytime soon. For greater efficiency to occur, all of these things must happen:

• Universal access to high-speed and advanced systems of pricing analysis.
• A universally accepted analysis system of pricing stocks.
• An absolute absence of human emotion in investment decision-making.
• The willingness of all investors to accept that their returns or losses will be exactly identical to all other market participants.

It is hard to imagine even one of these criteria of market efficiency ever being met.

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