types of hypothesis in economics

Top 4 Types of Hypothesis in Consumption (With Diagram)

The following points highlight the top four types of Hypothesis in Consumption. The types of Hypothesis are: 1. The Post-Keynesian Developments 2. The Relative Income Hypothesis 3. The Life-Cycle Hypothesis 4. The Permanent Income Hypothesis.

Hypothesis Type # 1. The Post-Keynesian Developments:

Data collected and examined in the post-Second World War period (1945-) confirmed the Keynesian consumption function.

Time series data collected over long periods showed that the relation between income and consumption was different from what cross-section data revealed.

In the short run, there was a non-proportional relation between income and consumption. But in the long run the relation was proportional. By constructing new aggregate data on consumption and income from 1869 and examining the same, Simon Kuznets discovered that the ratio of consumption to income was fairly stable from decade to decade, despite large increases in income over the period he studied.

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This contradicted Keynes’ conjecture that the average propensity to consume would fall with increases in income. Kuznets’ findings indicated that the APC is fairly constant over long periods of time. This fact presented a puzzle which is illustrated in Fig. 17.10.

Studies of cross-section (household) data and short time series confirmed the Keynesian hypothesis — the relationship between consumption and income, as indicated by the consumption function C s in Fig. 17.10.

But studies of long time series found that APC did not vary systematically with income, as is shown by the long-run consumption func­tion C L . The short-run consumption function has a falling APC, whereas the long-run consumption function has a constant APC.

Subsequent research on consumption at­tempted to explain how these two consump­tion functions could be consistent with each other.

Various attempts have been made to rec­oncile these conflicting evidences. In this context mention has to be made of James Duesenberry (who developed the relative income hypothesis), Ando, Brumberg and Modigliani (who developed the life cycle hypoth­esis of saving behaviour) and Milton Friedman who developed the permanent income hypothesis of consumption behaviour.

All these economists proposed explanations of these seemingly contradictory findings. These hypotheses may now be discussed one by one.

Hypothesis Type # 2. The Relative Income Hypothesis :

In 1949, James Duesenberry presented the relative income hypothesis. According to this hypothesis, saving (consumption) depends on relative income. The saving function is expressed as S t =f(Y t / Y p ), where Y t / Y p is the ratio of current income to some previous peak income. This is called relative income. Thus current consumption or saving is not a function-of current income but relative income.

Duensenberry pointed out that during depression when income falls consumption does not fall much. People try to protect their living standards either by reducing their past savings (or accumulated wealth) or by borrowing.

However as the economy gradually moves initially into the recovery and then in to the prosperity phase of the business cycle consumption does not rise even if income increases. People use a portion of their income either to restore the old saving rate or to repay their old debt.

Thus we see that there is a lack of symmetry in people’s consumption behaviour. People find it more difficult to reduce their consumption level than to raise it. This asymmetrical behaviour of consumers is known as the ratchet effect.

Thus if we observe a consumer’s short-run behaviour we find a non-proportional relation between income and consumption. Thus MPC is less than APC in the short run, as Keynes’s absolute income hypothesis has postulated. But if we study a consumer’s behaviour in the long run, i.e., over the entire business cycle we find a proportional relation between income and consumption. This means that in the long run MPC = APC.

Hypothesis Type # 3. The Life-Cycle Hypothesis :

In the late 1950s and early 1960s Franco Modigliani and his co-workers Albert Ando and Richard Brumberg related consumption expenditure to demography. Modigliani, in particular, emphasised that income varies systematically over peoples’ lives and that saving allows consumers to move income from early years of earning (when income is high) to later years after retirement when income is low.

This interpretation of household consumption behaviour forms the basis of his life-cycle hypothesis.

The life cycle hypothesis (henceforth LCH) represents an attempt to deal with the way in which consumers dispose off their income over time. In this hypothesis wealth is assigned a crucial role in consumption decision. Wealth includes not only property (houses, stocks, bonds, savings accounts, etc.) but also the value of future earnings.

Thus consumers visualise themselves as having a stock of initial wealth, a flow of income generated by that wealth over their lifetime and a target (which may be zero) as their end-of-life wealth. Consumption decisions are made with the whole series of financial flows in mind.

Thus, changes in wealth as reflected by unexpected changes in flow of earnings or unexpected movements in asset prices would have an impact on consumers’ spending decisions because they would enhance future earnings from property, labour or both. The theory has empirically testable implications for the relation between saving and age of a person as also for the role of wealth in influencing aggregate consumer spending.

The Hypothesis :

The main reason that an individual’s income varies is retirement. Since most people do not want their current living standard (as measured by consumption) to fall after retirement they save a portion of their income every year (over their entire service period). This motive for saving has an important implication for an individual’s consumption behaviour.

Suppose a representative consumer expects to live another T years, has wealth of W, and expects to earn income Y per year until he (she) retires R years from now. What should be the optimal level of consumption of the individual if he wishes to maintain a smooth level of consumption over his entire life?

The consumer’s lifetime endowments consist of initial wealth W and lifetime earnings RY. If we assume that the consumer divides his total wealth W + RY equally among the T years and wishes to consume smoothly over his lifetime then his annual consumption will be:

C = (W + RY)/T … (5)

This person’s consumption function can now be expressed as

C = (1/T)W + (R/T)Y

If all individuals plan their consumption in the same way then the aggregate consumption function is a replica of our representative consumer’s consumption function. To be more specific, aggregate consumption depends on both wealth and income. That is, the aggregate consumption function is

C = αW + βY …(6)

where the parameter α is the MPC out of wealth, and the parameter β is the MPC out of income.

Implications :

Fig. 17.11 shows the relationship between consumption and income in terms of the life cycle hypothesis. For any initial level of wealth w, the consumption function looks like the Keynesian function.

But the intercept αW which shows what would happen to consump­tion if income ever fell to zero, is not a constant, as is the term a in the Keynesian consumption function. Instead the intercept αW depends on the level of wealth. If W increases; the consumption line will shift up­ward parallely.

So one main prediction of the LCH is that consumption depends on wealth as well as income, as is shown by the intercept of the consumption function.

Solving the consumption puzzle:

The LCH can solve the consumption puzzle in a simple way.

According to this hypothesis, the APC is:

C/Y = α(W/Y) + β … (7)

Since wealth does not vary proportionately with income from person to person or from year to year, cross-section data (which show inter-individual differences in income and consumption over short periods) reveal that high income corresponds to a low APC. But in the long run, wealth and income grow together, resulting in a constant W/Y and a constant APC (as time-series show).

If wealth remains constant as in the short run the life cycle consumption function looks like the Keynesian consumption function, consumption function shifts upward as shown in Fig. 17.12. This prevents the APC from falling as income increases.

This means that the short-run consumption income relation (which takes wealth as constant) will not continue to hold in the long run when wealth increases. This is how the life cycle hypothesis (LCH) solves the consumption puzzle posed by Kuznets’ studies.

Other Predictions :

Another important prediction made by the LCH is that saving varies over a person’s lifetime. The LCH helps to link consumption and savings with the demo­graphic considerations, especially with the age distribution of the population.

The MPC out of life-time income changes with age. If a person has no wealth at the beginning of his service life, then he will accumulate wealth over his working years and then run down his wealth after his retirement. Fig. 17.13 shows the consumer’s income, consumption and wealth over his adult life.

If a consumer smoothest consumption over his life (as indicated by the horizontal consumption line), he will save and accumulate wealth during his working years and then dissave and run down his wealth after retirement. In other words, since people want to smooth consumption over their lives, the young — who are working — save, while the old — who have retired — dissave.

In the long run the consumption-income ratio is very stable, but in the short run it fluctuates. The life cycle approach explains this by pointing out that people seek to maintain a smooth profile of consumption even if their lifetime income flow is uneven, and thus emphasises the role of wealth in the consumption function.

Theory and Evidence: Do Old People Dissave?

Some recent findings present a genuine problem for the LCH. Old people are found not to dissave as much as the hypothesis predicts. This means that the elderly do not reduce their wealth as fast as one would expect, if they were trying to smooth their consumption over their remaining years of life.

Two reasons explain why the old people do not dissave as much as the LCH predicts:

(i) Precautionary saving:

The old people are very much concerned about unpredictable expenses. So there is some precautionary motive for saving which originates from uncertainty. This uncertainty arises from the fact that old people often live longer than they expect. So they have to save more than what an average span of retirement would warrant.

Moreover uncertainty arises due to the fact that the medical expenses of old people increase faster than their age. So some sort of Malthusian spectre is found to be operating in this case. While an old person’s age increases at an arithmetical progression his medical expenses increase in geometrical progression due to accelerated depreciation of human body and the stronger possibility of illness.

The old people are likely to respond to this uncertainty by saving more in order to be able to overcome these contingencies.

Of course, there is an offsetting consideration here. Due to the spread of health and medical insurance in recent years old people can protect themselves against uncertainties about medical expenses at a low cost (i.e., just by paying a small premium).

Now-a-days various insurance plans are offered by both government and private agencies (such as Medisave, Mediclaim, Medicare, etc.). Of course, the premium rate increases with age. As a result the old people are required to increase their saving rate to fulfill their contractual obligations.

However, to protect against uncertainty regarding lifespan, old people can buy annuities from insurance companies. For a fixed fee, annuities offer a stream of income over the entire life span of the recipient.

(ii) Leaving bequests:

Old people do not dissave because they want to leave bequests to their children. The reason is that they care about them. But altruism is not really the reason that parents leave bequests. Parents often use the implicit threat of disinheritance to induce a desirable pattern of behaviour so that children and grandchildren take more care of them or be more attentive.

Thus LCH cannot fully explain consumption behaviour in the long run. No doubt providing for retirement is an important motive for saving, but other motives, such as precautionary saving and bequest, are no less important in determining people’s saving behaviour.

Another explanation, which differs in details but entirely shares the spirit of the life cycle approach is the permanent income hypothesis of consumption. The hypothesis, which is the brainchild of Milton Friedman, argues that people gear their consumption behaviour to their permanent or long term consumption opportunities, not to their current level of income.

An individual does not plan consumption within a period solely on the basis of income within the period; rather, consumption is planned in relation to income over a longer period. It is to this hypothesis that we turn now. We may now turn to Friedman’s permanent income hypothesis, which suggests an alternative explanation of long-run income-consumption relationship.

Hypothesis Type # 4. The Permanent Income Hypothesis :

Milton Friedman’s permanent income hypothesis (henceforth PIH) presented in 1957, comple­ments Modigliani’s LCH. Both the hypotheses argue that consumption should not depend on current income alone.

But there is a difference of insight between the two hypotheses while the LCH emphasises that income follows a regular pattern over a person’s lifetime, the PIH emphasises that people experience random and temporary changes in their incomes from year to year.

The PIH, Friedman himself claims, ‘seems potentially more fruitful and in some measure more general” than the relative income hypothesis or the life-cycle hypothesis.

The idea of consumption spending that is geared to long-term average or permanent income is essentially the same as the life cycle theory. It raises two further questions. The first concerns the precise relationship between current consumption and permanent income. The second question is how to make the concept of present income operational, that is how to measure it.

The Basic Hypothesis :

According to Friedman the total measured income of an individual Y m has two compo­nents : permanent income Y p and transitory income Y t . That is, Y m – Y p + Y t .

Permanent income is that part of income which people expect to earn over their working life. Transitory income is that part of income which people do not expect to persist. In other words, while permanent income is average income, transitory income is the random deviation from that average.

Different forms of income have different degrees of persistence. While adequate investment in human capital (expenditure on training and education) provides a permanently higher income, good weather provides only transitorily higher income.

The PIH states that current consumption is not dependent solely on current disposable income but also on whether or not that income is expected to be permanent or transitory. The PIH argues that both income and consumption are split into two parts — permanent and transitory.

A person’s permanent income consists of such things as his long term earnings from employment (wages and salaries), retirement pensions and income derived from possessions of capital assets (interest and dividends).

The amount of a person’s permanent income will determine his permanent consumption plan, e.g., the size and quality of house he buys and, thus, his long term expenditure on mortgage repayments, etc.

Transitory income consists of short-term (temporary) overtime payments, bonuses and windfall gains from lotteries or stock appreciation and inheritances. Negative transitory income consists of short-term reduction in income arising from temporary unemployment and illness.

Transitory consumption such as additional holidays, clothes, etc. will depend upon his entire income. Long term consumption may also be related to changes in a person’s wealth, in particular the value of house over time. The economic significance of the PIH is that the short run level of consumption will be higher or lower than that indicated by the level of current disposable income.

According to Friedman consumption depends primarily on permanent income, because consumers use saving and borrowing to smooth consumption in response to transitory changes in income. The reason is that consumers spend their permanent income, but they save rather than spend most of their transitory income.

Since permanent income should be related to long run average income, this feature of the consumption function is clearly in line with the observed long run constancy of the consumption income ratio.

Let Y represent a consumer unit’s measured income for some time period, say, a year. This, according to Friedman, is the sum of two components : a permanent component (Y p ) and a transitory component (Y t ), or

Y = Y P + Y t …(8)

The permanent component reflects the effect of those factors that the unit regards as determining its capital value or wealth the non-human wealth it owns, the personal attributes of the earners in the unit, such as their training, ability, personality, the attributes of the economic activity of the earners, such as the occupation followed, the location of the economic activity, and so on.

The transitory component is to be interpreted as reflecting all ‘other’ factors, factors that are likely to be treated by the unit affected as ‘accident’ or ‘chance’ occurrences, for example, illness, a bad guess about when to buy or sell, windfall or chance gains from race or lotteries and so on. Permanent income is some sort of average.

Transitory income is a random variable. The difference between the two depends on how long the income persists. In other words, the distinction between the two is based on the degree of persistence. For example education gives an individual permanent income but luck — such as good weather — gives a farmer transitory income.

It may also be noted that permanent income cannot be zero or negative but transitory income can be.

Suppose a daily wage earner falls sick for a day or two and may not earn anything. So his transitory income is zero. Similarly if an individual sales a share in the stock exchange at a loss his transitory income is negative. Finally permanent income shows a steady trend but transitory income shows wide fluctuation(s).

Similarly, let C represent a consumer unit’s expenditures for some time period. It is also the sum of a permanent component (C p ) and a transitory component (C t ), so that

C = C p + C t … (9)

Some factors producing transitory components of consumption are: unusual sickness, a specifically favourable opportunity to purchase and the like. Permanent consumption is assumed to be the flow of utility services consumed by a group over a specific period.

The permanent income hypothesis is given by three simple equations (8), (9) and (10):

Y = Y p + Y t …(8)

C – C p + C t …(9)

C p = kY p , where k = f (r, W, u) …(10)

Here equation (6) defines a relation between permanent income and permanent consump­tion. Friedman specifies that the ratio between them is independent of the size of permanent income, but does depend on other variables in particular: (i) the rate of interest (r) or sets of rates of interest at which the consumer unit can borrow or lend; (ii) the relative importance of property and non-property income, symbolised by the ratio of non-human wealth to income (W) (iii) the factors symbolised by the random variable u determining the consumer unit’s tastes and preference for consumption versus additions to wealth. Equations (8) and (9) define the connection between the permanent components and the measured magnitudes.

Friedman assumes that the transistory components of income and consumption are uncorrelated with one another and with the corresponding permanent components, or

P ytyp = P ctcp = P ytct = 0 …(11)

where p stands for the correlation coefficient between the variables designated by the subscripts. The assumption that the third component in equation (11) — between the transitory components of income and consumption — is zero is indeed a strong assumption.

As Friedman says:

“The common notion that savings,…, are a ‘residue’ speaks strongly for the plausibility of the assumption. For this notion implies that consumption is determined by rather long-run considerations, so that any transitory changes in income lead primarily to additions to assets or to the use of previously accumulated balances rather than to corresponding changes in consumption.”

In Fig. 17.14 we consider the con­sumer units with a particular measured income, say which is above the mean measured income for the group as a whole — Y’. Given zero correlation be­tween permanent and transitory compo­nents of income, the average permanent income of those units is less than Y 0 ; that is, the average transitory component is positive.

The average consumption of units with a measured income Y 0 is, therefore, equal to their average perma­nent consumption. In Friedman’s hy­pothesis this is k times their average permanent income.

If Y 0 were not only the measured income of these units but also their permanent income, their mean consumption would be Y 0 or Y 0 E. Since their mean permanent income is less than their measured income (i.e., the transitory component of income is positive), their average consumption, Y 0 F, is less than Y 0 E.

By the same logic, for consumer units with an income equal to the mean of the group as a whole, or Y, the average transitory component of income as well as of consumption is zero, so the ordinate of the regression line is equal to the ordinate of the line 0E which gives the relation between Y p and C p .

For units with an income below the mean, the average transitory component of income is negative, so average measured consumption (CC”) is greater than the ordinate of 0E (BC’). The regression line (C = a + bY), therefore, intersects 0E at D, is above it to the left of D, and below it to the right of D.

If k is less than unity, permanent consumption is always less than permanent income. But measured consumption is not necessarily less than measured income. The line OH is a 45° line along which C = Y.

The vertical distance between this line and IF is average measured savings. Point J is called the ‘break-even’ point at which average measured savings are zero. To the left of J, average measured savings are negative, to the right, positive; as measured income increases so does the ratio of average measured savings to measured income.

Friedman’s hypothesis thus yields a relation between measured consumption and measured income that reproduces the broadest features of the corresponding regressions that have been computed from observed data. The point is that consumption expenditures seem to be proportional to disposable income in the long run.

In the short run, on the other hand, the consumption-income ratio fluctuates considerably. In sum, current consumption is related to some long-run measure of income (e.g., permanent income) while short-run fluctuations in income tend primarily to affect the level of saving.

Estimating Permanent Income :

Dornbusch and Fischer have defined permanent income as “the steady rate of consumption a person could maintain for the rest of his or her life, given the present level of wealth and income earned now and in the future.”

One might estimate permanent income as being equal to last year’s income plus some fraction of the change in income from last year to this year:

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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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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."

Definition of a Hypothesis

What it is and how it's used in sociology

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

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

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

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

Null Hypothesis

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

Alternative Hypothesis

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

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

Formulating a Hypothesis

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

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

Updated by Nicki Lisa Cole, Ph.D

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

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What Is the Life-Cycle Hypothesis (LCH)?

Understanding the life-cycle hypothesis, life-cycle hypothesis vs. keynesian theory, special considerations for the life-cycle hypothesis.

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What Is the Life-Cycle Hypothesis in Economics?

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

Investopedia / Zoe Hansen

The life-cycle hypothesis (LCH) is an economic theory that describes the spending and saving habits of people over the course of a lifetime. The theory states that individuals seek to smooth consumption throughout their lifetime by borrowing when their income is low and saving when their income is high.

The concept was developed by economists Franco Modigliani and his student Richard Brumberg in the early 1950s.

Key Takeaways

• The Life-Cycle Hypothesis (LCH) is an economic theory developed in the early 1950s that posits that people plan their spending throughout their lifetimes, factoring in their future income.
• A graph of the LCH shows a hump-shaped pattern of wealth accumulation that is low during youth and old age and high in middle age.
• One implication is that younger people have a greater capacity to take investment risks than older individuals who need to draw down accumulated savings.

The LCH assumes that individuals plan their spending over their lifetimes, taking into account their future income. Accordingly, they take on debt when they are young, assuming future income will enable them to pay it off. They then save during middle age in order to maintain their level of consumption when they retire.

A graph of an individual's spending over time thus shows a hump-shaped pattern in which wealth accumulation is low during youth and old age and high during middle age.

The LCH replaced an earlier hypothesis developed by economist John Maynard Keynes in 1937. Keynes believed that savings were just another good and that the percentage that individuals allocated to their savings would grow as their incomes rose. This presented a potential problem in that it implied that as a nation’s incomes grew, a savings glut would result, and aggregate demand and economic output would stagnate.

Another problem with Keynes' theory is that he did not address people's consumption patterns over time. For example, an individual in middle age who is the head of a family will consume more than a retiree. Although subsequent research has generally supported the LCH, it also has its problems.

The LCH has largely supplanted Keynesian economic thinking about spending and savings patterns.

The LCH makes several assumptions. For example, the theory assumes that people deplete their wealth during old age. Often, however, the wealth is passed on to children, or older people may be unwilling to spend their wealth. The theory also assumes that people plan ahead when it comes to building wealth, but many procrastinate or lack the discipline to save.

Another assumption is that people earn the most when they are of working age. However, some people choose to work less when they are relatively young and continue working part-time when they reach retirement age.

As a result, one implication is that younger people are more able to take on investment risks than older individuals, which remains a widely accepted tenet of personal finance.

Other assumptions of note are that those with high incomes are more able to save and have greater financial savvy than those on low incomes. People with low incomes may have credit card debt and less disposable income. Lastly, safety nets or means-tested benefits for aging adults may discourage people from saving as they anticipate receiving a higher social security payment when they retire.

Franco Modigliani. "Life cycle, individual thrift, and the wealth of nations." American Economic Review, 1986, Vol. 76, Issue 3, Pages 297-313.

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

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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13 Different Types of Hypothesis

There are 13 different types of hypothesis. These include simple, complex, null, alternative, composite, directional, non-directional, logical, empirical, statistical, associative, exact, and inexact.

A hypothesis can be categorized into one or more of these types. However, some are mutually exclusive and opposites. Simple and complex hypotheses are mutually exclusive, as are direction and non-direction, and null and alternative hypotheses.

Below I explain each hypothesis in simple terms for absolute beginners. These definitions may be too simple for some, but they’re designed to be clear introductions to the terms to help people wrap their heads around the concepts early on in their education about research methods .

Types of Hypothesis

Before you Proceed: Dependent vs Independent Variables

A research study and its hypotheses generally examine the relationships between independent and dependent variables – so you need to know these two concepts:

• The independent variable is the variable that is causing a change.
• The dependent variable is the variable the is affected by the change. This is the variable being tested.

Read my full article on dependent vs independent variables for more examples.

Example: Eating carrots (independent variable) improves eyesight (dependent variable).

1. Simple Hypothesis

A simple hypothesis is a hypothesis that predicts a correlation between two test variables: an independent and a dependent variable.

This is the easiest and most straightforward type of hypothesis. You simply need to state an expected correlation between the dependant variable and the independent variable.

You do not need to predict causation (see: directional hypothesis). All you would need to do is prove that the two variables are linked.

Simple Hypothesis Examples

2. complex hypothesis.

A complex hypothesis is a hypothesis that contains multiple variables, making the hypothesis more specific but also harder to prove.

You can have multiple independent and dependant variables in this hypothesis.

Complex Hypothesis Example

In the above example, we have multiple independent and dependent variables:

• Independent variables: Age and weight.
• Dependent variables: diabetes and heart disease.

Because there are multiple variables, this study is a lot more complex than a simple hypothesis. It quickly gets much more difficult to prove these hypotheses. This is why undergraduate and first-time researchers are usually encouraged to use simple hypotheses.

3. Null Hypothesis

A null hypothesis will predict that there will be no significant relationship between the two test variables.

For example, you can say that “The study will show that there is no correlation between marriage and happiness.”

A good way to think about a null hypothesis is to think of it in the same way as “innocent until proven guilty”[1]. Unless you can come up with evidence otherwise, your null hypothesis will stand.

A null hypothesis may also highlight that a correlation will be inconclusive . This means that you can predict that the study will not be able to confirm your results one way or the other. For example, you can say “It is predicted that the study will be unable to confirm a correlation between the two variables due to foreseeable interference by a third variable .”

Beware that an inconclusive null hypothesis may be questioned by your teacher. Why would you conduct a test that you predict will not provide a clear result? Perhaps you should take a closer look at your methodology and re-examine it. Nevertheless, inconclusive null hypotheses can sometimes have merit.

Null Hypothesis Examples

4. alternative hypothesis.

An alternative hypothesis is a hypothesis that is anything other than the null hypothesis. It will disprove the null hypothesis.

We use the symbol H A or H 1 to denote an alternative hypothesis.

The null and alternative hypotheses are usually used together. We will say the null hypothesis is the case where a relationship between two variables is non-existent. The alternative hypothesis is the case where there is a relationship between those two variables.

The following statement is always true: H 0 ≠ H A .

Let’s take the example of the hypothesis: “Does eating oatmeal before an exam impact test scores?”

We can have two hypotheses here:

• Null hypothesis (H 0 ): “Eating oatmeal before an exam does not impact test scores.”
• Alternative hypothesis (H A ): “Eating oatmeal before an exam does impact test scores.”

For the alternative hypothesis to be true, all we have to do is disprove the null hypothesis for the alternative hypothesis to be true. We do not need an exact prediction of how much oatmeal will impact the test scores or even if the impact is positive or negative. So long as the null hypothesis is proven to be false, then the alternative hypothesis is proven to be true.

5. Composite Hypothesis

A composite hypothesis is a hypothesis that does not predict the exact parameters, distribution, or range of the dependent variable.

Often, we would predict an exact outcome. For example: “23 year old men are on average 189cm tall.” Here, we are giving an exact parameter. So, the hypothesis is not composite.

But, often, we cannot exactly hypothesize something. We assume that something will happen, but we’re not exactly sure what. In these cases, we might say: “23 year old men are not on average 189cm tall.”

We haven’t set a distribution range or exact parameters of the average height of 23 year old men. So, we’ve introduced a composite hypothesis as opposed to an exact hypothesis.

Generally, an alternative hypothesis (discussed above) is composite because it is defined as anything except the null hypothesis. This ‘anything except’ does not define parameters or distribution, and therefore it’s an example of a composite hypothesis.

6. Directional Hypothesis

A directional hypothesis makes a prediction about the positivity or negativity of the effect of an intervention prior to the test being conducted.

Instead of being agnostic about whether the effect will be positive or negative, it nominates the effect’s directionality.

We often call this a one-tailed hypothesis (in contrast to a two-tailed or non-directional hypothesis) because, looking at a distribution graph, we’re hypothesizing that the results will lean toward one particular tail on the graph – either the positive or negative.

Directional Hypothesis Examples

7. non-directional hypothesis.

A non-directional hypothesis does not specify the predicted direction (e.g. positivity or negativity) of the effect of the independent variable on the dependent variable.

These hypotheses predict an effect, but stop short of saying what that effect will be.

A non-directional hypothesis is similar to composite and alternative hypotheses. All three types of hypothesis tend to make predictions without defining a direction. In a composite hypothesis, a specific prediction is not made (although a general direction may be indicated, so the overlap is not complete). For an alternative hypothesis, you often predict that the even will be anything but the null hypothesis, which means it could be more or less than H 0 (or in other words, non-directional).

Let’s turn the above directional hypotheses into non-directional hypotheses.

Non-Directional Hypothesis Examples

8. logical hypothesis.

A logical hypothesis is a hypothesis that cannot be tested, but has some logical basis underpinning our assumptions.

These are most commonly used in philosophy because philosophical questions are often untestable and therefore we must rely on our logic to formulate logical theories.

Usually, we would want to turn a logical hypothesis into an empirical one through testing if we got the chance. Unfortunately, we don’t always have this opportunity because the test is too complex, expensive, or simply unrealistic.

Here are some examples:

• Before the 1980s, it was hypothesized that the Titanic came to its resting place at 41° N and 49° W, based on the time the ship sank and the ship’s presumed path across the Atlantic Ocean. However, due to the depth of the ocean, it was impossible to test. Thus, the hypothesis was simply a logical hypothesis.
• Dinosaurs closely related to Aligators probably had green scales because Aligators have green scales. However, as they are all extinct, we can only rely on logic and not empirical data.

9. Empirical Hypothesis

An empirical hypothesis is the opposite of a logical hypothesis. It is a hypothesis that is currently being tested using scientific analysis. We can also call this a ‘working hypothesis’.

We can to separate research into two types: theoretical and empirical. Theoretical research relies on logic and thought experiments. Empirical research relies on tests that can be verified by observation and measurement.

So, an empirical hypothesis is a hypothesis that can and will be tested.

• Raising the wage of restaurant servers increases staff retention.
• Adding 1 lb of corn per day to cows’ diets decreases their lifespan.
• Mushrooms grow faster at 22 degrees Celsius than 27 degrees Celsius.

Each of the above hypotheses can be tested, making them empirical rather than just logical (aka theoretical).

10. Statistical Hypothesis

A statistical hypothesis utilizes representative statistical models to draw conclusions about broader populations.

It requires the use of datasets or carefully selected representative samples so that statistical inference can be drawn across a larger dataset.

This type of research is necessary when it is impossible to assess every single possible case. Imagine, for example, if you wanted to determine if men are taller than women. You would be unable to measure the height of every man and woman on the planet. But, by conducting sufficient random samples, you would be able to predict with high probability that the results of your study would remain stable across the whole population.

You would be right in guessing that almost all quantitative research studies conducted in academic settings today involve statistical hypotheses.

Statistical Hypothesis Examples

• Human Sex Ratio. The most famous statistical hypothesis example is that of John Arbuthnot’s sex at birth case study in 1710. Arbuthnot used birth data to determine with high statistical probability that there are more male births than female births. He called this divine providence, and to this day, his findings remain true: more men are born than women.
• Lady Testing Tea. A 1935 study by Ronald Fisher involved testing a woman who believed she could tell whether milk was added before or after water to a cup of tea. Fisher gave her 4 cups in which one randomly had milk placed before the tea. He repeated the test 8 times. The lady was correct each time. Fisher found that she had a 1 in 70 chance of getting all 8 test correct, which is a statistically significant result.

11. Associative Hypothesis

An associative hypothesis predicts that two variables are linked but does not explore whether one variable directly impacts upon the other variable.

We commonly refer to this as “ correlation does not mean causation ”. Just because there are a lot of sick people in a hospital, it doesn’t mean that the hospital made the people sick. There is something going on there that’s causing the issue (sick people are flocking to the hospital).

So, in an associative hypothesis, you note correlation between an independent and dependent variable but do not make a prediction about how the two interact. You stop short of saying one thing causes another thing.

Associative Hypothesis Examples

• Sick people in hospital. You could conduct a study hypothesizing that hospitals have more sick people in them than other institutions in society. However, you don’t hypothesize that the hospitals caused the sickness.
• Lice make you healthy. In the Middle Ages, it was observed that sick people didn’t tend to have lice in their hair. The inaccurate conclusion was that lice was not only a sign of health, but that they made people healthy. In reality, there was an association here, but not causation. The fact was that lice were sensitive to body temperature and fled bodies that had fevers.

12. Causal Hypothesis

A causal hypothesis predicts that two variables are not only associated, but that changes in one variable will cause changes in another.

A causal hypothesis is harder to prove than an associative hypothesis because the cause needs to be definitively proven. This will often require repeating tests in controlled environments with the researchers making manipulations to the independent variable, or the use of control groups and placebo effects .

If we were to take the above example of lice in the hair of sick people, researchers would have to put lice in sick people’s hair and see if it made those people healthier. Researchers would likely observe that the lice would flee the hair, but the sickness would remain, leading to a finding of association but not causation.

Causal Hypothesis Examples

13. exact vs. inexact hypothesis.

For brevity’s sake, I have paired these two hypotheses into the one point. The reality is that we’ve already seen both of these types of hypotheses at play already.

An exact hypothesis (also known as a point hypothesis) specifies a specific prediction whereas an inexact hypothesis assumes a range of possible values without giving an exact outcome. As Helwig [2] argues:

“An “exact” hypothesis specifies the exact value(s) of the parameter(s) of interest, whereas an “inexact” hypothesis specifies a range of possible values for the parameter(s) of interest.”

Generally, a null hypothesis is an exact hypothesis whereas alternative, composite, directional, and non-directional hypotheses are all inexact.

See Next: 15 Hypothesis Examples

This is introductory information that is basic and indeed quite simplified for absolute beginners. It’s worth doing further independent research to get deeper knowledge of research methods and how to conduct an effective research study. And if you’re in education studies, don’t miss out on my list of the best education studies dissertation ideas .

[1] https://jnnp.bmj.com/content/91/6/571.abstract

[2] http://users.stat.umn.edu/~helwig/notes/SignificanceTesting.pdf

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Animism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 10 Magical Thinking Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ Social-Emotional Learning (Definition, Examples, Pros & Cons)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ What is Educational Psychology?

2 thoughts on “13 Different Types of Hypothesis”

Wow! This introductionary materials are very helpful. I teach the begginers in research for the first time in my career. The given tips and materials are very helpful. Chris, thank you so much! Excellent materials!

You’re more than welcome! If you want a pdf version of this article to provide for your students to use as a weekly reading on in-class discussion prompt for seminars, just drop me an email in the Contact form and I’ll get one sent out to you.

When I’ve taught this seminar, I’ve put my students into groups, cut these definitions into strips, and handed them out to the groups. Then I get them to try to come up with hypotheses that fit into each ‘type’. You can either just rotate hypothesis types so they get a chance at creating a hypothesis of each type, or get them to “teach” their hypothesis type and examples to the class at the end of the seminar.

Cheers, Chris

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25.1: Major Theories in Macroeconomics

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Keynesian Theory

Keynesian theory posits that aggregate demand will not always meet the supply produced.

Learning objectives

• Explain the main tenets of Keynesian economics

Historical Background

John Maynard Keynes published a book in 1936 called The General Theory of Employment, Interest, and Money , laying the groundwork for his legacy of the Keynesian Theory of Economics. It was an interesting time for economic speculation considering the dramatic adverse effect of the Great Depression. Keynes’s concepts played a role in public economic policy under Roosevelt as well as during World War II, becoming the dominant perspective in Europe following the war.

John Maynard Keynes : John Maynard Keynes came to fame after publishing his economic theories during the Great Depression.

At the time, the primary school of economic thought was that of the classical economists (which is still a popular school of thought today). The central tenet of the classical argument says that supply can always create demand, and that surpluses will result in price reductions to the point of consumption. Put simply, people have infinite needs and the market will self-correct to the aggregate demands and available resources. This implies a hands-of public policy where markets are capable of taking care of themselves.

Keynes positioned his argument in contrast to this idea, stating that markets are imperfect and will not always self correct. Keynes theorized that natural inefficiencies in the market will see goods that are not met with demand. This wasted capital can result in market losses, unemployment, and market inefficiency (this was called ‘general glut’ in the classical model, when aggregate demand does not meet supply). Keynes insisted that markets do need moderate governmental intervention through fiscal policy (government investment in infrastructure) and monetary policy ( interest rates ).

Main Tenets

With this overview in mind, Keynesian Theory generally observes the following concepts:

• Unemployment: Under the classical model, unemployment is often attributed to high and rigid real wages. Keynes argues there is more complexity than that, specifically that societies are highly resistant to wage cuts and furthermore that reducing wages would pose a great threat to an economy. Specifically, cutting wages reduces spending and may result in a downwards spiral.
• Excessive Saving: Keynes’s concept here is somewhat complicated, but in short Keynes notes excessive saving as a threat and prospective cause of economic decline. This is because excessive saving leads to reduced investment and reduced spending, which drives down demand and the potential for consumption. This can be another spiraling issue, as money not being exchanged is actively reducing prospective employment, revenues, and future investments.
• Fiscal Policy: The key concept in fiscal policy for Keynes is ‘counter-cyclical’ fiscal policy, which is the expectation that governments can reduce the negative effects of the natural business cycle. This is, generally, achieved through deficit spending in recessions and suppression of inflation during boom times. Simply put, the government should try to curb the extremes of economic fluctuation through informed fiscal policy.
• The Multiplier Effect: This idea has in many ways already been implied in the atom, but inversely. Consider the unemployment and excessive savings problems, and how they stand to lead to spiraling decline. The other side of that coin is that positive economic situations can spiral upwards. Take for example a government investment in transportation, putting money in the pockets of various individuals who build trains and tracks. These individuals will spend that extra capital, putting money in the hands of other business (and this will continue). This is called the multiplier effect.
• IS-LM: While the IS-LM Model is a complicated byproduct of Keynesian economics, it can be summarized as the relationship between interest rates (y-axis) and the real economic output (x-axis). This is done through analyzing the invest-saving relationship (IS) in contrast to the liquidity preference and money supply relationship (LM), generating an equilibrium where certain interest rates and outputs will be generated.

While Keynesian Theory has been expounded upon significantly over the years, the important takeaway here is that aggregate demand (and thus the amount of supply consumed) is not a perfect system. Instead, demand is affected by various external forces that can create an inefficient market which will in turn affect employment, production, and inflation.

IS-LM Model : In this figure, the IS (Interest – Saving) curve is shifted outward in a way that raises both interest rates (i) and the ‘real’ economy (Y). The implication is that interest rates affect investment levels, and that these investment levels in turn affect the overall economy.

Monetarism focuses on the macroeconomic effects of the supply of money and the role of central banking on an economic system.

• Explain the main tenets of Monetarism

In the rise of monetarism as an ideology, two specific economists were critical contributors. Clark Warburton, in 1945, has been identified as the first thinker to draft an empirically sound argument in favor of monetarism. This was taken more mainstream by Milton Friedman in 1956 in a restatement of the quantity theory of money. The basic premise these two economists were putting forward is that the supply of money and the role of central banking play a critical role in macroeconomics.

The generation of this theory takes into account a combination of Keynesian monetary perspectives and Friedman’s pursuit of price stability. Keynes postulated a demand-driven model for currency; a perspective on printed money that was not beholden to the ‘ gold standard ‘ (or basing economic value off of rare metal). Instead, the amount of money in a given environment should be determined by monetary rules. Friedman originally put forward the idea of a ‘k-percent rule,’ which weighed a variety of economic indicators to determine the appropriate money supply.

Theoretically, the idea is actually quite straight-forward. When the money supply is expanded, individuals will be induced to higher spending. In turn, when the money supply retracted, individuals would limit their budgetary spending accordingly. This would theoretically provide some control over aggregate demand (which is one of the primary areas of disagreement between Keynesian and classical economists).

Monetarism began to deviate more from Keynesian economics however in the 70’s and 80’s, as active implementation and historical reflection began to generate more evidence for the monetarist view. In 1979 for example, Jimmy Carter appointed Paul Volcker as Chief of the Federal Reserve, who in turn utilized the monetarist perspective to control inflation. He eventually created a price stability, providing evidence that the theory was sound. In addition, Milton Friedman and Ann Schwartz analyzed the Great Depression in the context of monetarism as well, identifying a shortage of the money supply as a critical component of the recession.

The 1980s were an interesting transitional period for this perspective, as early in the decade (1980-1983) monetary policies controlling capital were attributed to substantial reductions in inflation (14% to 3%)(see ). However, unemployment and the rise of the use of credit are quoted as two alternatives to money supply control being the primary influence of the boom that followed 1983.

U.S. Inflation Rates : The inflation rates over time in the U.S. represent some of the evidence put forward by monetarist economists, stating that governmental control of the money supply allows for some control over inflation.

Counter Arguments

As these counter arguments in the 1980s began to arise, critics of monetarism became more mainstream. Of the current monetarism critics, the Austrian school of thought is likely the most well-known. The Austrian school of economic thought perceives monetarism as somewhat narrow-minded, not effectively taking into account the subjectivity involved in valuing capital. That is to say that monetarism seems to assume an objective value of capital in an economy, and the subsequent implications on the supply and demand.

Other criticisms revolve around international investment, trade liberalization, and central bank policy. This can be summarized as the effects of globalization, and the interdependence of markets (and consequently currencies). To manipulate money supply there will inherently be effects on other currencies as a result of relativity. This is particularly important in regards to the U.S. currency, which is considered a standard in international markets. Controlling supply and altering value may have effects on a variety of internal economic variables, but it will also have unintended consequences on external variables.

Austrian economic thought is about methodological individualism, or the idea that people will act in meaningful ways which can be analyzed.

• Explain the main tenets of Austrian economics

The Austrian school of economics originated in the 19th century in Vienna, Austria. While there were a variety of famous economists attributed to the early foundations and later expansions of the Austrian economic perspective, Carl Menger, Friedrich von Weiser, and Eugen von Bohm-Bawerk are widely recognized as critical early pioneers. The general perspective of Austrian economic thought is methodological individualism, or the recognition that people will act in meaningful ways which can be analyzed for trends.

Central Tenets

The Austrian school of thought provided enormous value to the economic climate, both as a foundation for future economics and as a deliberate counterpoint to more quantitative analysis. Of the most important ideologies, the following central tenets are:

• Opportunity Cost: This is a concept you are likely already familiar with, and one of the most important ideas in all of business and economics. Essentially, the price of a good must also incorporate the value sacrificed of the next best alternative. Basically each choice a consumer or business makes intrinsically has the cost of not being able to make an alternative choice.
• Capital and Interest: Largely in response to Karl Marx’s labor theories, Austrian economist Bohm-Bawerk identified the building blocks of interest rates and profit are supply and demand alongside time preference. In short, present consumption is more valuable than future consumption (the time value of money).
• Inflation: The idea that prices and wages must rise as a result of increased money supply is inflation (note: this is different that price inflation). Simply put, more money in the system without a higher demand for that money will drive down the relative value of each dollar.
• Business Cycles: The Austrian business cycle theory (ABCT) is the simple observation that the issuance of credit (by banks) creates economic fluctuations that tend to be cyclical (see ). In simple terms, banks will lend out money at rates lower than the risk in which that money will be used. So when businesses fail more often than they succeed, thus losing interest as opposed to accruing it, will struggle to repay their debts. When the banks call in those debts the business cannot pay, creating negative business cycles.
• The Organizing Power of Markets: The idea of this concept is that no one person knows what the appropriate price of a good should be. Instead, markets naturally generate incentives to identify optimal price points. This negates the ideas of socialism common at the time, as communist systems will be unable to identify the appropriate exchange value of each good.

As you can see from the above points, this school of economics is largely about making qualitative observations of the markets. These observations are absolutely critical in understanding the theoretical landscape, but difficult to enact in practice.

Austrian economists are often criticized for ignoring arithmetic or statistical ways to measure and analyze economics. Indeed, Austrian economists do not often place much weight on concepts such as econometrics, experimental economics, and aggregate macroeconomic analysis. In this sense, the Austrian school of thought is something of an outsider relative to other perspectives (i.e. classical, Keynesian, etc.).

Paul Krugman criticized Austrian economics as lacking explicit models of analysis, or essentially a lack of clarity in their approach. This results in inadvertent blind spots. This is a sensible criticism in many ways, as the fundamental idea behind this economic theory is that it is driven by individuals and individuals are not always rational (indeed, they are quite often irrational). As a result of this, Austrian economics often rests on the integration of social sciences (psychology, sociology, etc.) to explain preferences and consumer behavior, which is often counter-intuitive. As a result, it is very difficult to accurately measure and provide tangible proof of the efficacy of Austrian models.

Alternative Views

Neoclassical and neo-Keynesian ideas can be coupled and referred to as the neoclassical synthesis, combining alternative views in economics.

• Summarize neoclassical and Neo-Keynesian economics

The history of different economic schools of thought have consistently generated evolving theories of economics as new data and new perspectives are taken into consideration. The two most well-known schools, classical economics and Keynesian economics, have been adapting to incorporate new information and ideas from one another as well as lesser known schools of economics (Chicago, Austrian, etc.). These different perspectives have motivated economists to generate the neoclassical and neo-Keynesian perspectives. The neoclassical perspective, in conjunction with Keynesian ideas, is referred to as the neoclassical synthesis, which is largely considered the ‘mainstream’ economic perspective.

Neoclassical

In approaching Neoclassical economics, it is most important to keep in mind the following three principles:

• People have rational preferences in the context of options or outcomes that can be identified and associated with a given value (usually monetary). In short, people make smart choices regarding how they spend their money.
• Individuals maximize utility and firms maximize profit. People will try to get the most from their money while corporations will try to invest their time and assets to capture the highest margin.
• People act independently based upon comprehensive and relevant information. People are influenced by rational forces (mostly information and logic), and will make the best personal purchasing decisions based upon this.

A brief timeline of classical to neoclassical perspectives would begin with thought processes put forward by Adam Smith and David Ricardo (alongside many others). The basic idea is that aggregate demand will adjust to supply, and that value theory and distribution will reflect this rational, cost of production model. The next phase was the observation that consumer goods demonstrated a relative value based on utility, which could deviate from consumer to consumer. The final phase, and most central to the advent of the neoclassical perspective, is the introduction of marginalism. Marginalism notes that economic participants make decisions based on marginal utility or margins. For example, a company hiring a new employee will not think of the fixed value of that employee, but instead the marginal value of adding that employee (usually in regards to profitability).

Neo-Keynesian

Neo-Keynesian economics is often confused with ‘New Keynesian’ economics (which attempts to provide microeconomic foundation to Keynesian views, particularly in light of stagflation in the 1970s). Neo-Keynesian economics is actually the formalization and coordination of Keynes’s writings by a number of other economists (most notably John Hicks, Franco Modigliani, and Paul Samuelson). Much of the conceptual value is captured in the previous atoms on Keynesian views, but the substantial value of a few neo-Keynesian ideas is worth reiterating:

• IS/LM Model: This model was put forward by John Hicks in order to capture the inherent relationship between investment and savings (IS) relative to liquidity and the overall money supply (LM) (see ). The implications of this graph pertain to the static representation of monetary policy and the effects on an economic system.
• Phillips Curve: Another important model following Keynes’s publications is the Phillips Curve, put forward by William Phillips in 1958. The idea here was also largely Keynesian, revolving around the relationship between inflation and unemployment (see ).This implies a trade off between inflation rates and the creation of employment, which governments could consider in policy making. Stagflation (economic stagnation and inflation simultaneously) created issues with this however, necessitating New Keynesian ideas (as discussed briefly above).

When learning about these economic perspectives, it is important to understand the value they add to one another and the overall efficacy of all economic theory. Economists are often the product of multiple schools of thought, and don’t fit neatly into one school or another.

• John Maynard Keynes published a book in 1936 called The General Theory of Employment, Interest, and Money, laying the groundwork for his legacy of the Keynesian Theory of Economics.
• Keynes positioned his argument in contrast to this idea, stating that markets are imperfect and will not always self correct.
• Keynes believed that wage reductions in recessions and excessive savings were potential threats to an economy.
• Keynesian theory expects fiscal policy to offset business cycles (employ counter-cyclical strategies).
• Clark Warburton, in 1945, has been identified as the first thinker to draft an empirically sound argument in favor of monetarism. This was taken more mainstream by Milton Friedman in 1956.
• More money in the system results in higher spending and vice verse. This would theoretically provide some control over aggregate demand.
• Historical implementation of monetarism demonstrated some correlation with control over inflation rates and increased economic performance. This could have been a result of other factors however.
• The Austrian school of economic thought perceives monetarism as somewhat narrow-minded, not effectively taking into account the subjectivity involved in valuing capital.
• Due to the globalization of the economy, monetarism may have a negative impact on external economies. This is particularly true of the U.S., whose capital is an international standard.
• The Austrian school of economics is one of the oldest economic perspectives, originating in the 19th century in Vienna.
• Austrian economics is attributed for the identification of opportunity cost, capital and interest, inflation, business cycles and the organizing power of markets.
• Austrian economists do not often place much weight on concepts such as econometrics, experimental economics, and aggregate macroeconomic analysis. In this sense, the Austrian school of thought is something of an outsider relative to other perspectives (i.e. classical, Keynesian, etc. ).
• Paul Krugman criticized Austrian economics as lacking explicit models of analysis, or essentially a lack of clarity in their approach. This results in inadvertent blind spots.
• The history of different economic schools of thought have consistently generated evolving theories of economics as new data and new perspectives are taken into consideration.
• The neoclassical perspective in conjunction with Keynesian ideas is referred to as the neoclassical synthesis, which is largely considered the ‘mainstream’ economic perspective.
• A critical difference between classical and neoclassical perspectives is the introduction of marginalism. Marginalism notes that economic participants make decisions based on marginal utility or margins.
• Neo- Keynesian economics is the formalization and coordination of Keynes’s writings by a number of other economists (most notably John Hicks, Franco Modigliani and Paul Samuelson).
• The important to understand that these economic perspectives add value to one another and the overall efficacy of all economic theory.
• fiscal policy : Government policy that attempts to influence the direction of the economy through changes in government spending or taxes.
• monetary policy : The process of controlling the supply of money in an economy, often conducted by central banks.
• Keynesian : Of or pertaining to an economic theory based on the ideas of John Maynard Keynes, as put forward in his book The General Theory of Employment, Interest, and Money.
• Monetarism : The doctrine that economic systems are controlled by variations in the supply of money.
• gold standard : A monetary system where the value of circulating money is linked to the value of gold.
• Opportunity cost : The cost of any activity measured in terms of the value of the next best alternative forgone (that is not chosen).
• time value of money : The time value of money is the principle that a certain currency amount of money today has a different buying power (value) than the same currency amount of money in the future.
• stagflation : Inflation accompanied by stagnant growth, unemployment or recession.
• static : Unchanging; that cannot or does not change.

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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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