hypothesis meaning in accounting

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Accounting Theory: Definition & Overview

Have you been wondering about accounting theory? Accounting is an important part of every business. It helps make sure financial transactions get recorded , and assists with things like payroll.

Accounting theory has the purpose of guiding accounting processes , so that everything is as accurate as possible. So , what are some of the accounting theory applications? W hat are some of the practices? There are some financial reporting principles to be aware of in the accounting industry.

We put this guide together to outline everything you need to know . Keep reading to learn all about the study of accounting theory and how it gets applied. You’ll be able to have effective accounting skills in no time.

KEY TAKEAWAYS

• Accounting theory guides financial reporting and accounting processes. 
• It involves methodologies, frameworks, and assumptions that get used in financial reporting. 
• Accounting theory evolves and adapts to environmental changes and new trends.

What Is Accounting Theory? 

Accounting theory works in a few ways, and is made up of different frameworks, assumptions, and methodologies. All of this gets put into place so reporting financials are correct and accurate.

There are some foundations in accounting theory that guide accounting practices. The Financial Accounting Standards Board (FASB) put together a framework of accounting theories to use. 

The FASB is an independent entity. Their focus is on outlining the key objectives and framework for reporting financials that is used by both private and public businesses. It’s the logical reasoning behind accounting practices, and as new accounting practices and procedures develop, accounting theory evolves.

Key Elements of Accounting Theory

The first key element in accounting theory is usefulness. Its main intention is to ensure financial statements make important information available. This helps to make more informed business decisions. 

This means that there’s also some flexibility to accounting theory. By being flexible, it can adapt to environmental changes. The framework of accounting theory also has a few other aspects to know about. 

These include being consistent, reliable, relevant, and comparable. To help guide these aspects, the generally accepted accounting principles (GAAP) were created. 

Following GAAP ensures financial statements, and how they’re prepared, are consistent. Consistency leads to higher accuracy and makes it so that past financials are comparable to the financials of other companies.

Accounting Theory Assumptions 

Within the blocks of accounting theory, there are also some assumptions. Accounting professionals follow these assumptions: 

• That a business needs to be separate from its owners or creditors
• That there’s a belief that the business won’t go bankrupt and will always exist
• That financial statements get prepared with dollar amounts, not units of production
• That all financial statements get prepared either monthly or annually

Are There Any Special Considerations? 

The process of accounting dates back to the 15th century; however, economies and businesses have greatly evolved since then. This means that accounting theory also evolves and adapts when required. 

This can include new practices, or discovering gaps in reporting mechanisms. It can also include the advancement of new technological trends. Professional accountants contribute their knowledge to help with these changes. For example, a certified public accountant (CPA) can lend their expertise.

When this happens, new methodologies can be created to stay up-to-date, and businesses can better navigate and understand new accounting standards. All of this leads to the evolution of accounting theory.

The elements in accounting theory consist of a few different things. These include certain frameworks, assumptions, and methodologies. All of these things get used when reporting financials. The purpose is to ensure financials are consistent, accurate, and comparable. 

Accounting theory gets used by businesses to make more informed decisions. The main aspect of accounting theory is its usefulness. These frameworks get designed with a few other things in mind: being reliable, consistent, relevant, and comparable. 

Following basic accounting theory makes for more effective and accurate accounting practices. This can come in handy for things like an income statement or statement of cash flows.

Frequently Asked Questions

There are five main principles of accounting. They include the accrual principle, the historic cost principle, matching principle , conservatism principle , and the principle of substance over form. Following these principles allows for better accounting practices and accurate financial statements.

This relates to a few different things. It starts with equal treatment for all interested parties. It also includes having true and accurate accounting statements that aren’t misrepresented. It also includes having a fair, unbiased, and completely impartial presentation.

With the positive approach in accounting theory, the goal is to explain a process. This means using knowledge and understanding of accounting , and implementing accounting policies to deal with certain conditions. Other accounting fundamentals are also implemented.

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3.1 Describe Principles, Assumptions, and Concepts of Accounting and Their Relationship to Financial Statements

If you want to start your own business, you need to maintain detailed and accurate records of business performance in order for you, your investors, and your lenders, to make informed decisions about the future of your company. Financial statements are created with this purpose in mind. A set of financial statements includes the income statement, statement of owner’s equity, balance sheet, and statement of cash flows. These statements are discussed in detail in Introduction to Financial Statements . This chapter explains the relationship between financial statements and several steps in the accounting process. We go into much more detail in The Adjustment Process and Completing the Accounting Cycle .

Accounting Principles, Assumptions, and Concepts

In Introduction to Financial Statements , you learned that the Financial Accounting Standards Board (FASB) is an independent, nonprofit organization that sets the standards for financial accounting and reporting, including generally accepted accounting principles (GAAP) , for both public- and private-sector businesses in the United States.

As you may also recall, GAAP are the concepts, standards, and rules that guide the preparation and presentation of financial statements. If US accounting rules are followed, the accounting rules are called US GAAP. International accounting rules are called International Financial Reporting Standards (IFRS) . Publicly traded companies (those that offer their shares for sale on exchanges in the United States) have the reporting of their financial operations regulated by the Securities and Exchange Commission (SEC) .

You also learned that the SEC is an independent federal agency that is charged with protecting the interests of investors, regulating stock markets, and ensuring companies adhere to GAAP requirements. By having proper accounting standards such as US GAAP or IFRS, information presented publicly is considered comparable and reliable. As a result, financial statement users are more informed when making decisions. The SEC not only enforces the accounting rules but also delegates the process of setting standards for US GAAP to the FASB.

Some companies that operate on a global scale may be able to report their financial statements using IFRS. The SEC regulates the financial reporting of companies selling their shares in the United States, whether US GAAP or IFRS are used. The basics of accounting discussed in this chapter are the same under either set of guidelines.

Ethical Considerations

Auditing of publicly traded companies.

When a publicly traded company in the United States issues its financial statements, the financial statements have been audited by a Public Company Accounting Oversight Board (PCAOB) approved auditor. The PCAOB is the organization that sets the auditing standards, after approval by the SEC. It is important to remember that auditing is not the same as accounting. The role of the Auditor is to examine and provide assurance that financial statements are reasonably stated under the rules of appropriate accounting principles. The auditor conducts the audit under a set of standards known as Generally Accepted Auditing Standards. The accounting department of a company and its auditors are employees of two different companies. The auditors of a company are required to be employed by a different company so that there is independence.

The nonprofit Center for Audit Quality explains auditor independence: “Auditors’ independence from company management is essential for a successful audit because it enables them to approach the audit with the necessary professional skepticism.” 1 The center goes on to identify a key practice to protect independence by which an external auditor reports not to a company’s management, which could make it more difficult to maintain independence, but to a company’s audit committee. The audit committee oversees the auditors’ work and monitors disagreements between management and the auditor about financial reporting. Internal auditors of a company are not the auditors that provide an opinion on the financial statements of a company. According to the Center for Audit Quality, “By law, public companies’ annual financial statements are audited each year by independent auditors—accountants who examine the data for conformity with U.S. Generally Accepted Accounting Principles (GAAP).” 2 The opinion from the independent auditors regarding a publicly traded company is filed for public inspection, along with the financial statements of the publicly traded company.

The Conceptual Framework

The FASB uses a conceptual framework , which is a set of concepts that guide financial reporting. These concepts can help ensure information is comparable and reliable to stakeholders. Guidance may be given on how to report transactions, measurement requirements, and application on financial statements, among other things. 3

IFRS Connection

Gaap, ifrs, and the conceptual framework.

The procedural part of accounting—recording transactions right through to creating financial statements—is a universal process. Businesses all around the world carry out this process as part of their normal operations. In carrying out these steps, the timing and rate at which transactions are recorded and subsequently reported in the financial statements are determined by the accepted accounting principles used by the company.

As you learned in Role of Accounting in Society , US-based companies will apply US GAAP as created by the FASB, and most international companies will apply IFRS as created by the International Accounting Standards Board (IASB). As illustrated in this chapter, the starting point for either FASB or IASB in creating accounting standards, or principles, is the conceptual framework. Both FASB and IASB cover the same topics in their frameworks, and the two frameworks are similar. The conceptual framework helps in the standard-setting process by creating the foundation on which those standards should be based. It can also help companies figure out how to record transactions for which there may not currently be an applicable standard. Though there are many similarities between the conceptual framework under US GAAP and IFRS, these similar foundations result in different standards and/or different interpretations.

Once an accounting standard has been written for US GAAP, the FASB often offers clarification on how the standard should be applied. Businesses frequently ask for guidance for their particular industry. When the FASB creates accounting standards and any subsequent clarifications or guidance, it only has to consider the effects of those standards, clarifications, or guidance on US-based companies. This means that FASB has only one major legal system and government to consider. When offering interpretations or other guidance on application of standards, the FASB can utilize knowledge of the US-based legal and taxation systems to help guide their points of clarification and can even create interpretations for specific industries. This means that interpretation and guidance on US GAAP standards can often contain specific details and guidelines in order to help align the accounting process with legal matters and tax laws.

In applying their conceptual framework to create standards, the IASB must consider that their standards are being used in 120 or more different countries, each with its own legal and judicial systems. Therefore, it is much more difficult for the IASB to provide as much detailed guidance once the standard has been written, because what might work in one country from a taxation or legal standpoint might not be appropriate in a different country. This means that IFRS interpretations and guidance have fewer detailed components for specific industries as compared to US GAAP guidance.

The conceptual framework sets the basis for accounting standards set by rule-making bodies that govern how the financial statements are prepared. Here are a few of the principles, assumptions, and concepts that provide guidance in developing GAAP.

Revenue Recognition Principle

The revenue recognition principle directs a company to recognize revenue in the period in which it is earned; revenue is not considered earned until a product or service has been provided. This means the period of time in which you performed the service or gave the customer the product is the period in which revenue is recognized.

There also does not have to be a correlation between when cash is collected and when revenue is recognized. A customer may not pay for the service on the day it was provided. Even though the customer has not yet paid cash, there is a reasonable expectation that the customer will pay in the future. Since the company has provided the service, it would recognize the revenue as earned, even though cash has yet to be collected.

For example, Lynn Sanders owns a small printing company, Printing Plus. She completed a print job for a customer on August 10. The customer did not pay cash for the service at that time and was billed for the service, paying at a later date. When should Lynn recognize the revenue, on August 10 or at the later payment date? Lynn should record revenue as earned on August 10. She provided the service to the customer, and there is a reasonable expectation that the customer will pay at the later date.

Expense Recognition (Matching) Principle

The expense recognition principle (also referred to as the matching principle ) states that we must match expenses with associated revenues in the period in which the revenues were earned. A mismatch in expenses and revenues could be an understated net income in one period with an overstated net income in another period. There would be no reliability in statements if expenses were recorded separately from the revenues generated.

For example, if Lynn earned printing revenue in April, then any associated expenses to the revenue generation (such as paying an employee) should be recorded on the same income statement. The employee worked for Lynn in April, helping her earn revenue in April, so Lynn must match the expense with the revenue by showing both on the April income statement.

Cost Principle

The cost principle , also known as the historical cost principle , states that virtually everything the company owns or controls ( assets ) must be recorded at its value at the date of acquisition. For most assets, this value is easy to determine as it is the price agreed to when buying the asset from the vendor. There are some exceptions to this rule, but always apply the cost principle unless FASB has specifically stated that a different valuation method should be used in a given circumstance.

The primary exceptions to this historical cost treatment, at this time, are financial instruments, such as stocks and bonds, which might be recorded at their fair market value. This is called mark-to-market accounting or fair value accounting and is more advanced than the general basic concepts underlying the introduction to basic accounting concepts; therefore, it is addressed in more advanced accounting courses.

Once an asset is recorded on the books, the value of that asset must remain at its historical cost, even if its value in the market changes. For example, Lynn Sanders purchases a piece of equipment for $40,000. She believes this is a bargain and perceives the value to be more at $60,000 in the current market. Even though Lynn feels the equipment is worth $60,000, she may only record the cost she paid for the equipment of $40,000.

Full Disclosure Principle

The full disclosure principle states that a business must report any business activities that could affect what is reported on the financial statements. These activities could be nonfinancial in nature or be supplemental details not readily available on the main financial statement. Some examples of this include any pending litigation, acquisition information, methods used to calculate certain figures, or stock options. These disclosures are usually recorded in footnotes on the statements, or in addenda to the statements.

Separate Entity Concept

The separate entity concept prescribes that a business may only report activities on financial statements that are specifically related to company operations, not those activities that affect the owner personally. This concept is called the separate entity concept because the business is considered an entity separate and apart from its owner(s).

For example, Lynn Sanders purchases two cars; one is used for personal use only, and the other is used for business use only. According to the separate entity concept, Lynn may record the purchase of the car used by the company in the company’s accounting records, but not the car for personal use.

Conservatism

This concept is important when valuing a transaction for which the dollar value cannot be as clearly determined, as when using the cost principle. Conservatism states that if there is uncertainty in a potential financial estimate, a company should err on the side of caution and report the most conservative amount. This would mean that any uncertain or estimated expenses/losses should be recorded, but uncertain or estimated revenues/gains should not. This understates net income, therefore reducing profit. This gives stakeholders a more reliable view of the company’s financial position and does not overstate income.

Monetary Measurement Concept

In order to record a transaction, we need a system of monetary measurement , or a monetary unit by which to value the transaction. In the United States, this monetary unit is the US dollar. Without a dollar amount, it would be impossible to record information in the financial records. It also would leave stakeholders unable to make financial decisions, because there is no comparability measurement between companies. This concept ignores any change in the purchasing power of the dollar due to inflation.

Going Concern Assumption

The going concern assumption assumes a business will continue to operate in the foreseeable future. A common time frame might be twelve months. However, one should presume the business is doing well enough to continue operations unless there is evidence to the contrary. For example, a business might have certain expenses that are paid off (or reduced) over several time periods. If the business will stay operational in the foreseeable future, the company can continue to recognize these long-term expenses over several time periods. Some red flags that a business may no longer be a going concern are defaults on loans or a sequence of losses.

Time Period Assumption

The time period assumption states that a company can present useful information in shorter time periods, such as years, quarters, or months. The information is broken into time frames to make comparisons and evaluations easier. The information will be timely and current and will give a meaningful picture of how the company is operating.

For example, a school year is broken down into semesters or quarters. After each semester or quarter, your grade point average (GPA) is updated with new information on your performance in classes you completed. This gives you timely grading information with which to make decisions about your schooling.

A potential or existing investor wants timely i nformation by which to measure the performance of the company, and to help decide whether to invest. Because of the time period assumption, we need to be sure to recognize revenues and expenses in the proper period. This might mean allocating costs over more than one accounting or reporting period.

The use of the principles, assumptions, and concepts in relation to the preparation of financial statements is better understood when looking at the full accounting cycle and its relation to the detailed process required to record business activities ( Figure 3.2 ).

Concepts In Practice

Tax cuts and jobs act.

In 2017, the US government enacted the Tax Cuts and Jobs Act. As a result, financial stakeholders needed to resolve several issues surrounding the standards from GAAP principles and the FASB. The issues were as follows: “Current Generally Accepted Accounting Principles (GAAP) requires that deferred tax liabilities and assets be adjusted for the effect of a change in tax laws or rates,” and “implementation issues related to the Tax Cuts and Jobs Act and income tax reporting.” 4

In response, the FASB issued updated guidance on both issues. You can explore these revised guidelines at the FASB website (https://www.fasb.org/taxcutsjobsact#section_1).

The Accounting Equation

Introduction to Financial Statements briefly discussed the accounting equation, which is important to the study of accounting because it shows what the organization owns and the sources of (or claims against) those resources. The accounting equation is expressed as follows:

Recall that the accounting equation can be thought of from a “sources and claims” perspective; that is, the assets (items owned by the organization) were obtained by incurring liabilities or were provided by owners. Stated differently, everything a company owns must equal everything the company owes to creditors (lenders) and owners (individuals for sole proprietors or stockholders for companies or corporations).

In our example in Why It Matters , we used an individual owner, Mark Summers, for the Supreme Cleaners discussion to simplify our example. Individual owners are sole proprietors in legal terms. This distinction becomes significant in such areas as legal liability and tax compliance. For sole proprietors, the owner’s interest is labeled “owner’s equity.”

In Introduction to Financial Statements , we addressed the owner’s value in the firm as capital or owner’s equity . This assumed that the business is a sole proprietorship. However, for the rest of the text we switch the structure of the business to a corporation, and instead of owner’s equity, we begin using stockholder’s equity , which includes account titles such as common stock and retained earnings to represent the owners’ interests. The primary reason for this distinction is that the typical company can have several to thousands of owners, and the financial statements for corporations require a greater amount of complexity.

As you also learned in Introduction to Financial Statements , the accounting equation represents the balance sheet and shows the relationship between assets, liabilities, and owners’ equity (for sole proprietorships/individuals) or common stock (for companies).

You may recall from mathematics courses that an equation must always be in balance. Therefore, we must ensure that the two sides of the accounting equation are always equal. We explore the components of the accounting equation in more detail shortly. First, we need to examine several underlying concepts that form the foundation for the accounting equation: the double-entry accounting system, debits and credits, and the “normal” balance for each account that is part of a formal accounting system.

Double-Entry Bookkeeping

The basic components of even the simplest accounting system are accounts and a general ledger . An account is a record showing increases and decreases to assets, liabilities, and equity—the basic components found in the accounting equation. As you know from Introduction to Financial Statements , each of these categories, in turn, includes many individual accounts, all of which a company maintains in its general ledger. A general ledger is a comprehensive listing of all of a company’s accounts with their individual balances.

Accounting is based on what we call a double-entry accounting system , which requires the following:

• Each time we record a transaction, we must record a change in at least two different accounts. Having two or more accounts change will allow us to keep the accounting equation in balance.
• Not only will at least two accounts change, but there must also be at least one debit and one credit side impacted.
• The sum of the debits must equal the sum of the credits for each transaction.

In order for companies to record the myriad of transactions they have each year, there is a need for a simple but detailed system. Journals are useful tools to meet this need.

Debits and Credits

Each account can be represented visually by splitting the account into left and right sides as shown. This graphic representation of a general ledger account is known as a T-account . The concept of the T-account was briefly mentioned in Introduction to Financial Statements and will be used later in this chapter to analyze transactions. A T-account is called a “T-account” because it looks like a “T,” as you can see with the T-account shown here.

A debit records financial information on the left side of each account. A credit records financial information on the right side of an account. One side of each account will increase and the other side will decrease. The ending account balance is found by calculating the difference between debits and credits for each account. You will often see the terms debit and credit represented in shorthand, written as DR or dr and CR or cr , respectively. Depending on the account type, the sides that increase and decrease may vary. We can illustrate each account type and its corresponding debit and credit effects in the form of an expanded accounting equation . You will learn more about the expanded accounting equation and use it to analyze transactions in Define and Describe the Expanded Accounting Equation and Its Relationship to Analyzing Transactions .

As we can see from this expanded accounting equation, Assets accounts increase on the debit side and decrease on the credit side. This is also true of Dividends and Expenses accounts. Liabilities increase on the credit side and decrease on the debit side. This is also true of Common Stock and Revenues accounts. This becomes easier to understand as you become familiar with the normal balance of an account.

Normal Balance of an Account

The normal balance is the expected balance each account type maintains, which is the side that increases. As assets and expenses increase on the debit side, their normal balance is a debit. Dividends paid to shareholders also have a normal balance that is a debit entry. Since liabilities, equity (such as common stock), and revenues increase with a credit, their “normal” balance is a credit. Table 3.1 shows the normal balances and increases for each account type.

When an account produces a balance that is contrary to what the expected normal balance of that account is, this account has an abnormal balance . Let’s consider the following example to better understand abnormal balances.

Let’s say there were a credit of $4,000 and a debit of $6,000 in the Accounts Payable account. Since Accounts Payable increases on the credit side, one would expect a normal balance on the credit side. However, the difference between the two figures in this case would be a debit balance of $2,000, which is an abnormal balance. This situation could possibly occur with an overpayment to a supplier or an error in recording.

We define an asset to be a resource that a company owns that has an economic value. We also know that the employment activities performed by an employee of a company are considered an expense, in this case a salary expense. In baseball, and other sports around the world, players’ contracts are consistently categorized as assets that lose value over time (they are amortized).

For example, the Texas Rangers list “Player rights contracts and signing bonuses-net” as an asset on its balance sheet. They decrease this asset’s value over time through a process called amortization . For tax purposes, players’ contracts are treated akin to office equipment even though expenses for player salaries and bonuses have already been recorded. This can be a point of contention for some who argue that an owner does not assume the lost value of a player’s contract, the player does. 5

• 1 Center for Audit Quality. Guide to Public Company Auditing . https://www.iasplus.com/en/binary/usa/aicpa/0905caqauditguide.pdf
• 2 Center for Audit Quality. Guide to Public Company Auditing . https://www.iasplus.com/en/binary/usa/aicpa/0905caqauditguide.pdf
• 3 Financial Accounting Standards Board. “The Conceptual Framework.” http://www.fasb.org/jsp/FASB/Page/BridgePage&cid=1176168367774
• 4 Financial Accounting Standards Board (FASB). “Accounting for the Tax Cuts and Jobs Act.” https://www.fasb.org/taxcutsjobsact#section_1
• 5 Tommy Craggs. “MLB Confidential, Part 3: Texas Rangers.” Deadspin. August 24, 2010. https://deadspin.com/5619951/mlb-confidential-part-3-texas-rangers

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• Authors: Mitchell Franklin, Patty Graybeal, Dixon Cooper
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• Book title: Principles of Accounting, Volume 1: Financial Accounting
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A Beginner’s Guide to Hypothesis Testing in Business

• 30 Mar 2021

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

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

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

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What Is Hypothesis Testing?

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

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

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

Hypothesis Testing in Business

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

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

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

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

Related: 9 Fundamental Data Science Skills for Business Professionals

Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

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

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

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

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

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

2. Significance Level and P-Value

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

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

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

3. One-Sided vs. Two-Sided Testing

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

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

4. Sampling

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

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

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

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

Learn How to Perform Hypothesis Testing

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

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

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

About the Author

What is Hypothesis Testing?

Hypothesis testing steps, stating the null hypothesis and alternative hypothesis, what are type i and type ii errors, hypothesis testing example, more resources, hypothesis testing.

A statistical test to support your hypothesis

Hypothesis Testing is a method of statistical inference. It is used to test if a statement regarding a population parameter is statistically significant. Hypothesis testing is a powerful tool for testing the power of predictions.

A Financial Analyst , for example, might want to make a prediction of the mean value a customer would pay for her firm’s product. She can then formulate a hypothesis, for example, “The average value that customers will pay for my product is larger than $5.” To statistically test this question, the firm owner could use hypothesis testing. This example is further explored down below.

Hypothesis Testing is a critical part of the scientific method, which is a systematic approach to assessing theories through observation. A good theory is one that can make accurate predictions. For an analyst who makes predictions, hypothesis testing is a rigorous way of backing up his prediction with statistical analysis.

Here are the steps for hypothesis testing:

• State the null hypothesis ( H 0 ) and the alternative hypothesis ( H a ).
• Consider the statistical assumptions being made. Evaluate if these assumptions are coherent with the underlying population being evaluated. For example, is assuming the underlying distribution as a normal distribution sensible?
• Determine the appropriate probability distribution and select the appropriate test statistic.
• Select the significance level commonly denoted by the Greek letter alpha (α). This is the probability threshold for which the null hypothesis will be rejected.
• Based on the significance level and on the appropriate test, state the decision rule.
• Collect the observed sample data, and use it to calculate the test statistic.
• Based on your results, you should either reject the null hypothesis or fail to reject the null hypothesis. This is known as the statistical decision.
• Consider any other economic issues that are applied to the problem. These are non-statistical considerations that need to be considered for a decision. For example, sometimes, societal cultural shifts lead to changes in consumer behavior. This must be taken into consideration in addition to the statistical decision for a final decision.

The Null Hypothesis is usually set as what we don’t want to be true. It is the hypothesis to be tested. Therefore, the Null Hypothesis is considered to be true until we have sufficient evidence to reject it. If we reject the null hypothesis, we are led to the alternative hypothesis.

Going back to our initial example of the business owner who is looking for some customer insight. Her null hypothesis would be:

H 0 : The average value customers are willing to pay for my product is smaller than or equal to $5

H 0 : µ ≤ 5

( µ = the population mean)

The alternative hypothesis would then be what we are evaluating, so, in this case, it would be:

H a : The average value customers are willing to pay for the product is greater than $5

H a : µ > 5

It is important to emphasize that the alternative hypothesis will only be considered if the sample data that we gather provide evidence for it.

The binary nature of our decision, to reject or fail to reject the null hypothesis, gives rise to two possible errors. The table below illustrates all of the possible outcomes. A Type I Error arises when a true Null Hypothesis is rejected . The probability of making a Type I Error is also known as the level of significance of the test, which is commonly referred to as alpha (α). So, for example, if a test that has its alpha set as 0.01, there is a 1% probability of rejecting a true null hypothesis or a 1% probability of making a Type I Error.

A Type II Error arises when you fail to reject a False Null Hypothesis . The probability of making a Type II Error is commonly denoted by the Greek letter beta (β). β is used to define the Power of a Test, which is the probability of correctly rejecting a false null hypothesis. The Power of a Test  is defined as 1-β . A test with more Power is more desirable, as there is a lower probability of making a Type II Error. However, there is a tradeoff between the probability of making a Type I Error and the probability of making a Type II Error.

Let’s go back to the business owner example. Let us remember the question that we are trying to answer:

Q: “Will customers pay, on average, more than $5 for our product?”

1. We have set above both the null and alternative hypothesis

2. For this example, let us assume that the firm sells organic apple juice boxes. They are consumed by a wide range of consumers of all ages, income levels, and cultural backgrounds. So, given that our product is widely used by a diverse group of consumers, assuming a normal distribution is fair.

3. Let us assume that by getting samples from our consumers, we will manage to get over 100 observations. Given we are confident with our assumption of a normal distribution for the underlying population and have a large number of observations, we will use a z-test.

4. We want to be confident of our result, so let us pick our significance level as α = 5%. This will provide strong evidence of our result.

5. We are using a z-test with a significance level, and the null hypothesis is  µ ≤ 5, so our rejection point will be  z 0.05  =1.645 . This means that if the z score calculated from our sample is greater than 1.645, we reject the null hypothesis. 

6. Now assume that we have collected our data and that from our sample of 100 observations, the mean price that customers are willing to pay for our juices is $5.02  and that the sample standard deviation was $0.10 . We can now calculate the z-score for our sample where we get a value of 2 given by  [(5.02 – 5) / ( 0.1/  √ 100)].

7. Given our calculated z is greater than  z 0.05  =1.645,  we have strong evidence to reject the null hypothesis at a 5% significance level. We are then in favor of the alternative hypothesis that t he average value customers are willing to pay for the product is greater than $5. 

8. We now need to take into consideration any economic or qualitative issues that are not addressed through the statistical process. These are usually non-quantifiable variables that have to be addressed when making a decision based on the findings. For example, if the biggest competitor was going to lower the price of the competing product significantly, that may lower the average value consumers are willing to pay for your product.

If you want to learn more about topics related to Hypothesis Testing, check out resources on the Royal Statistics Society website . To keep learning and advancing your career, the following CFI resources will also be helpful:

• Delphi Method
• Cointegration
• Durbin Watson Statistic
• Fibonacci Numbers
• AVERAGE Excel Function
• See all data science resources
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Positive Accounting Theory (PAT)

• Part 11.1 - Summary of Qualitative Characteristics of GENERALLY ACCEPTED ACCOUNTING PRINCIPLES (GAAP)
• Part 11.2 - How and When to Recognize Revenues & Expenses in Accrual Accounting
• Part 11.3 - Functions in the Purchasing Process and how to Segregate Purchasing Duties
• Part 11.4 - Three Steps to Determining and Applying Materiality
• Part 11.5 - Assertions of Management about Economic Events in the Business
• Part 11.6 - Bank Accounting - Types of Bank Accounts, Cash Receipts & Disbursements, Disclosures Required for Cash Accounting
• Part 11.7 - Objectives of Internal Controls set by Management
• Part 11.8 - How to Test Internal Controls of an Organization
• Part 11.9 - Positive Accounting Theory (PAT)
• Part 11.11 - Accounting Information - Complex Commodity & Information Asymmetry

Positive Accounting Theory tries to make good predictions of real world events and translate them to accounting transactions. While normative theories tend to recommend what should be done, Positive Theories try to explain and predict

o Actions such as which accounting policies firms will choose o How firms will react to newly proposed accounting standards

• Its overall intention is to understand and predict the choice of accounting policies across differing firms. It recognizes that economic consequences exist.

• Under PAT, firms want to maximize their prospects for survival, so they organize themselves efficiently.

• Firms are viewed as the accumulation of the contracts they have entered into.

• In relation to PAT, because there is a need to be efficient, the firm will want to minimize costs associated with contracts. Examples of contract costs are negotiation, renegotiation, and monitoring costs. Contract costs involve accounting variables as contracts can be stipulated in terms of accounting information such as net income, and financial ratios.

• The firm will choose the accounting policies that best acknowledge the need for minimization of contract costs.

• PAT recognizes that changing circumstances require managers to have flexibility in choosing accounting policies. • This brings forward the problem of “opportunistic behavior”. This occurs when the actions of management are to better their own personal interests.

• With this in mind, the optimal set of accounting policies are described as a compromise between fixing accounting policies to minimize contract costs and providing flexibility in times of changing circumstances (considering the effects of opportunistic behavior).

The Three Hypotheses of Positive Accounting Theory

Positive Accounting Theory has three hypotheses around which its predictions are organized.

1. Bonus plan hypothesis

• Managers of firms with bonus plans are more likely to choose accounting procedures that shift reported earnings from future periods to the current period. By doing so, they can increase their bonuses for the current year.

2. Debt covenant hypothesis

• The closer a firm is to violating accounting-based debt covenants, the more likely the firm manager is to select accounting procedures that shift reported earnings from future periods to the current period.

• By increasing current earnings, the company is less likely to violate debt covenants, and management has minimized its constraints in running the company

3. Political cost hypothesis

• The greater the political costs faced by the firm, the more likely the manager is to choose accounting procedures that defer reported earnings from current to future periods.

• High profitability can lead to increased political “heat”, and can lead to new taxes or regulations esp. for large firms which may be held to higher reporting standards

How to Achieve Positive Accounting Theory

• Changing accounting policies • Managing discretionary accruals • Timing of adoption of new accounting standards • Changing real variables--R&D, advertising, repairs & maintenance • SPEs (Enron), capitalize operating expenses (WorldCom)

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Home > Books > Accounting and Corporate Reporting - Today and Tomorrow

Accounting Choices in Corporate Financial Reporting: A Literature Review of Positive Accounting Theory

Submitted: 22 December 2016 Reviewed: 03 April 2017 Published: 20 September 2017

DOI: 10.5772/intechopen.68962

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Accounting and Corporate Reporting - Today and Tomorrow

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This chapter aims to put light on the positive accounting theory and related empirical studies and identify its broad contributions to the accounting research. Our objective is to provide a review of positive accounting literature in order to synthesize findings, identify areas of controversy in the literature, and evaluate critiques. Positive research in accounting started coming to prominence around the mid-1960s and had been a vector of paradigm shift within the financial accounting research in the 1970s and 1980s. The positive accounting theory is developed by Watts and Zimmerman and is based on work undertaken in economics and is heavily dependent on the efficient market hypothesis, the capital assets pricing model, and agency theory. The three key hypotheses are bonus plan hypothesis, debt hypothesis, and political cost hypothesis. Nevertheless, PAT has been subjected to severe and numerous criticisms from different perspectives, which are critiques on research methods, its theoretical foundations, its logic on economics' basis, and its reference to philosophy of science. PAT and its hypotheses will continue to be a rich field of empirical research and the basic questions that it raises are still relevant today.

• positive accounting theory
• accounting choice
• management compensation hypothesis
• debt hypothesis
• political cost hypothesis

Author Information

İdil kaya *.

• Galatasaray University, Istanbul, Turkey

*Address all correspondence to: [email protected]

1. Introduction

Academic studies on the factors that affect a firm's accounting choices triggered a paradigm change in accounting research, altering the nature of literature from prescriptive to predictive. The construct of the new paradigm was first articulated by Ross Watts and Jerold Zimmerman with the publication of their revolutionary articles in the Accounting Review —“Towards a Positive Theory of the Determination of Accounting Standards” in 1978 and “The Demand for and Supply of Accounting Theories: The Market for Excuses” in 1979.

The term “Positive Accounting Theory” has come to practise to refer to the accounting theory developed and named by Watts and Zimmerman. The authors seek to appreciate and explain the concept of economic consequences of the interests of managers and financial accounting and reporting. In other words, their major aim is to explain and predict why managers and accountants choose particular accounting methods in preference to others. Furthermore, they assert that firm's attributes, such as leverage and size, are predictive variables of the firm's accounting choice.

In fact, positive research in accounting started coming to prominence around the mid-1960s and had been a vector of paradigm shift within the financial accounting research in the 1970s and 1980s. The term “positive” refers to the theory that attempts to explain and make good predictions of particular phenomena. The positive accounting theory (PAT) relied in great part on work undertaken in economics and was heavily reliant on the efficient market hypothesis, the capital assets pricing model, and agency theory.

PAT has led to a large amount of empirical studies. Positive researchers empirically test their predictions around the bonus plan hypothesis, the debt covenant hypothesis, and the political cost hypothesis. These hypotheses can be used in two distinguished forms of positive accounting theory. The first form is the opportunistic form asserting that managers in electing accounting procedures react to maximize the wealth, and the second form is the efficiency form for good corporate governance.

PAT has been subjected to severe and numerous criticisms from different perspectives, which are critiques on research method, economics base, and reference to philosophy of science. It is said that PAT seeks to predict and explain why managers choose to adopt particular accounting methods in preference to others but says nothing as to which method a firm should use.

We believe that PAT and its hypotheses will continue to be a rich field of empirical research and the basic questions that it raises are still relevant today. This chapter aims to put light on the PAT and related empirical studies and identify its broad contributions to the accounting research. Our objective is to provide a review of extant literature in order to synthesize findings, identify areas of controversy in the literature, and evaluate critiques.

Our literature review is organized around ideas of PAT, its hypotheses, supporters and followers, and finally critiques of this theory. The remaining part of this chapter proceeds as follows: We first examine the forces that give rise to this theory. We then investigate its foundations using the works of Watts and Zimmerman. We describe how empirical studies added unique insights into its development. Some criticisms are evaluated. Finally, we outline and discuss the significant contribution of PAT to our understanding of corporate reporting practices. We conclude that this theory has generated several useful insights on managers' reporting decisions.

2. The paradigm change in accounting research: the origins of positive accounting theory

In this section, we examine the forces and the publications that had a major impact on the emergence of PAT. This theory is based on work undertaken in economics and is heavily dependent on the efficient market hypothesis, the capital assets pricing model, and agency theory.

Positive research began in early 1960s and opened a new era in accounting literature, using economic models and statistical processing in empirical studies. The first serious discussions and analyses of positive research on accounting emerged in late 1960s with the pioneering studies of Ball and Brown [ 1 ] and Beaver [ 2 ]. These two seminal publications provide significant evidence of the information content in accounting earnings announcements, i.e., the earnings reflect some of the information in security prices. They gave rise to a huge literature of capital markets research [ 3 ].

A significant number of academic publications investigated the determinants of the shift in paradigm from narrative to positive research. Major findings offered by these studies are as follows.

Research methodologies have been developed based on the “hypothesis formulating and testing” [ 4 , 5 ].

With the emergence of computers, large new databases of financial information would be readily accessible for researchers [ 4 , 6 ].

The concept of “economic consequences” has been investigated. This concept is defined by Zeff as “the impact of accounting reports on the decision-making behaviour of business, governments, and creditors” [ 7 , 8 ].

New academic journals have been established and they adopt the selection policy of empirical researches [ 9 ].

The development of behavioural science enabled to analyse managers' accounting choices [ 6 , 9 ].

Generous research grants have been provided to new generation of accounting researchers that applied empirical research methods [ 9 ].

It is said that two reports on US business education were the impetus for those changes [ 4 , 5 ]. In 1959, R.A. Gordon and James E. Howell published “Higher education for business” and Franck C. Pierson published “The education of American Business men”. The former report was commissioned by Ford Foundation and the latter by Carnegie Foundation. Besides their recommendations on teaching methods, these authors stressed the need to develop research based on the formulation and testing of the hypotheses. They also describe the resources necessary to advance the level of business studies.

Another significant explanation of the PAT's development is the strong influence of several academic works on positive economic theory, efficient markets hypothesis, CAPM, agency theory, and capital markets researches ( Table 1 ). Watts and Zimmerman aimed to develop an economic-based accounting theory and they advance an empirical methodology that focus on economics-based explanations and predictions of accounting practice. Boland and Gordon assert that this economic-based accounting theory is a combination of Milton Friedman's instrumentalism and Paul Samuelson's positivism [ 15 ]. They also add that Watts and Zimmerman practise the methodology as that of the Chicago School economists [ 6 , 15 ].

Table 1.

Academic literatures that were the impetus for PAT.

In 1976, the publication of Jensen and Merckling's article on agency theory had a major impact on PAT [ 14 ]. In agency theory, the firm is analysed as “a nexus of contracts” and this concept is accepted by positive accounting research. The contracts are produced with the aim of guarantee that all parties, acting in their own self-interest, are at the same time motivated towards maximizing the firm's value. PAT accentuates the function of accounting in reducing agency costs and its essential role in an efficient corporate governance structure [ 4 ].

3. The development of positive accounting theory

In this section, we examine the development of the PAT, the contribution of major works of Watts and Zimmerman, and the hypotheses of this theory.

The construct of PAT was first articulated by Watts and Zimmerman and popularized in their book: positive accounting theory [ 6 , 16 ]. Table 2 shows major works of Watts and Zimmerman in this issue. They adopted the label “positive” from the economics to distinguish accounting research aimed at understanding accounting from research directed at generating prescriptions. They investigated the role of accounting theory in determining accounting practice and build a theory intending to be a positive theory (Watts & Zimmerman, p. 274) [ 5 ], i.e.,

“a theory capable of explaining the factors determining the extant accounting literature, predicting how research will change as the underlying factors change, and explaining the role of theories in the determination of accounting standards. It is not normative or prescriptive”.

Table 2.

Major works of Watts and Zimmerman.

Watts and Zimmerman reviewed the theory and methodology of the economic-based literature in accounting in their prominent book dated 1986 [ 6 ]. In this book written and used for second year M.B.A and Ph.D. audience, the authors point the important role of efficient market hypothesis in accounting research; they use CAPM as the valuation method. They explain the methodology of the empirical studies in the development of the literature. They also provide analyses end syntheses on forecasting earnings, contracting process, compensation plans, debt contracts, political process, empirical tests of accounting choice, stock price tests of the theory, and the theory's application to auditing [ 6 ].

According to Watts and Zimmerman, the “property rights” theory adopted by positive accounting researchers assumes that the firm is a nexus of contracts between self-interested individuals. PAT highlighted the importance of contracting costs, including information, agency, bankruptcy, and lobbying costs [ 5 , 6 ].

In 1990, after more than a decade since the publication of 1978 and 1979 articles, the authors examined and evaluated the evolution and state of PAT and criticisms of positive accounting research in their article “Positive Accounting Theory: A Ten Year Perspective”, in the accounting review. They emphasized that their two pioneering papers contributed to a literature that has uncovered empirical regularities in accounting practice and they responded to most of the published critiques [ 5 ]. In evaluating the contribution of this article to the literature, Watts and Zimmerman asset that:

“The literature explains why accounting is used and provides a framework for predicting accounting choices. Choices are not made in terms of "better measurement" of some accounting construct, such as earnings. Choices are made in terms of individual objectives and the effects of accounting methods on the achievement of those objectives”.

Watts and Zimmerman identified three essential hypotheses. These are bonus plan hypothesis (or management compensation hypothesis), the debt/equity hypothesis (or debt hypothesis), and political cost hypothesis [ 5 ]. According to management compensation hypothesis, managers with bonus plans anchored to earnings are more likely to adopt accounting methods that increase current period's reported income. The debt hypothesis predicts that the higher the firm's debt/equity ratio, the more likely managers use accounting methods that increase earnings. As far as political costs hypothesis is concerned, it is assumed that if managers are under political scrutiny, they are likely to adopt accounting methods that reduce reported income [ 4 ].

4. Literature relating to the PAT

In this section, we examine the PAT literature. A considerable amount of literature has been published on PAT. Numerous empirical studies tested its hypotheses, provided important evidence, and contributed to the theory.

PAT literature focuses on management's motives for financial reporting choices, using economic models and statistical processing, when there are agency costs and information asymmetry. It attempts to explain and predict firm accounting choices as a part of the firm's overall need to minimize its cost of capital and other contracting costs, applying methods and techniques from economics. Opportunistic attitudes and behaviours of managers and their impacts on accounting policy choices have been investigated widely in positive research and this led to a rich body of empirical studies on earnings management. A wide range of the literature incorporates both ex ante contracting efficiency incentives with ex post redistributive effects. The methodology of this literature is the methodology of economics, finance, and science generally [ 5 ]. Table 3 provides an overview of these empirical researches and their research area.

Table 3.

Literature constructed on the PAT.

Beattie et al. state that this literature implicitly assumes that the market is inefficient and relies on bottom line accounting numbers and does not show interest in methods used to produce them [ 18 ]. According to Healy and Palepu (p. 419) [ 19 ],

”Empirical studies of positive accounting studies test whether managers make accounting method changes or accrual estimates to reduce the costs of violating bond covenants written in terms of accounting numbers, to increase the value of earnings-based bonuses under compensation contracts, or to reduce the likelihood of implicit or explicit taxes”.

On the other hand, Healy and Palepu assert that PAT studies generated several interesting empirical regularities regarding management accounting choice but there is ambiguity on the interpretation of this evidence [ 19 ].

5. Criticisms from different perspectives

In this section, we summarize and analyse the literature having critical comments on PAT. The literature developed since the first publication of Watts and Zimmerman articles in 1978.

PAT has been subject to a continuous and endless stream of criticisms since it first emerged in late 1970s. The critiques are from different perspectives. These are critiques related to its theoretical foundations, its logic on economics' basis, its research methods, and critiques on its reference to philosophy of science [ 15 , 35 ]. It has been defended that this theory is scientifically wrong and its predictions do not always hold. Christenson (p. 18) asserts that [ 36 ]:

“By arguing that their theories admit exceptions, Watts and Zimmerman condemn them as insignificant and useless”.

In an examination of PAT methodology with a critical look, Christenson argues that he prefers to use the name “the Rochester School” referring to authors' affiliation instead of PAT [ 36 ]. Furthermore, he asserts that this discipline should be denominated “sociology of accounting” since it is about describing, predicting, and explaining the behaviours of managers and accountants. Table 4 presents an overview of some criticisms.

Table 4.

Some criticisms of the PAT.

R. J. Chambers, preeminent normative theorist, criticises the PAT in an aggressive manner, begins his article with the so-called positive accounting theory and continue with the label “PA cult” referring to positive accounting theorists [ 37 ]. Chambers [ 37 ]asserts that:

“The verbal adornments of the cult — 'positive', 'empirical', 'scientific', 'economics based' and so on — its rituals, its congregations, its sanctions and its cohesion, drew a galaxy of followers into orbit about the Chicago-Rochester axis”.

The other major criticisms are as follows:

The theory does not provide prescription to improve accounting practice [ 4 , 40 ].

Its fundamental assumption that all action is driven by self-interest is flawy.

It focuses only in a single motive. Complex nature of shareholders and managers behaviour is not regarded in the analysis [ 4 , 38 ].

Measurements and proxies being used in its empirical researches have a simplistic nature [ 38 ].

The banking and global financial crisis in 2008 raised doubts on the efficient market hypothesis [ 4 ].

6. Conclusion

Positive research began in early 1960s and triggered a paradigm shift in accounting literature, using economic models and statistical processing in empirical studies. The PAT is developed by Watts and Zimmerman and is based on work undertaken in economics and is heavily dependent on the efficient market hypothesis, the capital assets pricing model and agency theory. Watts and Zimmerman founded Journal of Accounting and Economics in 1978. The three key hypotheses are bonus plan hypothesis, debt hypothesis, and political cost hypothesis.

Management compensation contracts, capital structure of the firm and its exposure to political scrutiny have been the main areas of researches that are concerned with explaining and predicting accounting practice. These three aspects of the theory-oriented main stream researches in accounting allowed accounting researchers to expand the boundaries of their studies to align with theories in the field of economics and management. Positive researchers introduced new rational from the economics literature to analyse the implications of the efficient market hypothesis for disclosure regulation, to investigate the stock price effects of changes in accounting procedures, and to study the variables that are related to contract political costs.

PAT has been also subjected to severe and numerous criticisms on its research methods, its theoretical foundations, its logic on economics' basis, and its reference to philosophy of science.

Acknowledgments

The author is grateful for the support provided by Galatasaray University Research Fund. [Grant number 16.102.002].

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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

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

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

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

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

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

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

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

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

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

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

• proposition
• supposition

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

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

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

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

Examples of hypothesis in a Sentence

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

Word History

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

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

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

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“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 11 Aug. 2024.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

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What is Hypothesis? Definition, Meaning, Characteristics, Sources

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

• What is Hypothesis?

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

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

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

Table of Content

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

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

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

Hypothesis Definition

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

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

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

A hypothesis is a proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined. – Webster’s New International Dictionary of the English Language (1956)

Meaning of Hypothesis

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

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

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

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

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

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

Characteristics of Hypothesis

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

Conceptual Clarity

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

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

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

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

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

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

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

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

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

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

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

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

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

Sources of Hypothesis

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

Observation

State of knowledge, continuity of research.

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

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

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

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

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

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

Null and Alternative Hypothesis

Null hypothesis.

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

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

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

Alternative Hypothesis

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

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

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Efficient Market Hypothesis (EMH)

Written by True Tamplin, BSc, CEPF®

Reviewed by subject matter experts.

Updated on July 12, 2023

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Table of contents, efficient market hypothesis (emh) overview.

The Efficient Market Hypothesis (EMH) is a theory that suggests financial markets are efficient and incorporate all available information into asset prices.

According to the EMH, it is impossible to consistently outperform the market by employing strategies such as technical analysis or fundamental analysis.

The hypothesis argues that since all relevant information is already reflected in stock prices, it is not possible to gain an advantage and generate abnormal returns through stock picking or market timing.

The EMH comes in three forms: weak, semi-strong, and strong, each representing different levels of market efficiency.

While the EMH has faced criticisms and challenges, it remains a prominent theory in finance that has significant implications for investors and market participants.

Types of Efficient Market Hypothesis

The Efficient Market Hypothesis can be categorized into the following:

Weak Form EMH

The weak form of EMH posits that all past market prices and data are fully reflected in current stock prices.

Therefore, technical analysis methods, which rely on historical data, are deemed useless as they cannot provide investors with a competitive edge. However, this form doesn't deny the potential value of fundamental analysis.

Semi-strong Form EMH

The semi-strong form of EMH extends beyond historical prices and suggests that all publicly available information is instantly priced into the market.

This includes financial statements, news releases, economic indicators, and other public disclosures. Therefore, neither technical analysis nor fundamental analysis can yield superior returns consistently.

Strong Form EMH

The most extreme version of EMH, the strong form, asserts that all information, both public and private, is fully reflected in stock prices.

Even insiders with privileged information cannot consistently achieve higher-than-average market returns. This form, however, is widely criticized as it conflicts with securities regulations that prohibit insider trading .

Assumptions of the Efficient Market Hypothesis

Three fundamental assumptions underpin the Efficient Market Hypothesis.

All Investors Have Access to All Publicly Available Information

This assumption holds that the dissemination of information is perfect and instantaneous. All market participants receive all relevant news and data about a security or market simultaneously, and no investor has privileged access to information.

All Investors Have a Rational Expectation

In EMH, it is assumed that investors collectively have a rational expectation about future market movements. This means that they will act in a way that maximizes their profits based on available information, and their collective actions will cause securities' prices to adjust appropriately.

Investors React Instantly to New Information

In an efficient market, investors instantaneously incorporate new information into their investment decisions. This immediate response to news and data leads to swift adjustments in securities' prices, rendering it impossible to "beat the market."

Implications of the Efficient Market Hypothesis

The EMH has several implications across different areas of finance.

Implications for Individual Investors

For individual investors, EMH suggests that "beating the market" consistently is virtually impossible. Instead, investors are advised to invest in a well-diversified portfolio that mirrors the market, such as index funds.

Implications for Portfolio Managers

For portfolio managers , EMH implies that active management strategies are unlikely to outperform passive strategies consistently. It discourages the pursuit of " undervalued " stocks or timing the market.

Implications for Corporate Finance

In corporate finance, EMH implies that a company's stock is always fairly priced, meaning it should be indifferent between issuing debt and equity . It also suggests that stock splits , dividends , and other financial decisions have no impact on a company's value.

Implications for Government Regulation

For regulators , EMH supports policies that promote transparency and information dissemination. It also justifies the prohibition of insider trading.

Criticisms and Controversies Surrounding the Efficient Market Hypothesis

Despite its widespread acceptance, the EMH has attracted significant criticism and controversy.

Behavioral Finance and the Challenge to EMH

Behavioral finance argues against the notion of investor rationality assumed by EMH. It suggests that cognitive biases often lead to irrational decisions, resulting in mispriced securities.

Examples include overconfidence, anchoring, loss aversion, and herd mentality, all of which can lead to market anomalies.

Market Anomalies and Inefficiencies

EMH struggles to explain various market anomalies and inefficiencies. For instance, the "January effect," where stocks tend to perform better in January, contradicts the EMH.

Similarly, the "momentum effect" suggests that stocks that have performed well recently tend to continue performing well, which also challenges EMH.

Financial Crises and the Question of Market Efficiency

The Global Financial Crisis of 2008 raised serious questions about market efficiency. The catastrophic market failure suggested that markets might not always price securities accurately, casting doubt on the validity of EMH.

Empirical Evidence of the Efficient Market Hypothesis

Empirical evidence on the EMH is mixed, with some studies supporting the hypothesis and others refuting it.

Evidence Supporting EMH

Several studies have found that professional fund managers, on average, do not outperform the market after accounting for fees and expenses.

This finding supports the semi-strong form of EMH. Similarly, numerous studies have shown that stock prices tend to follow a random walk, supporting the weak form of EMH.

Evidence Against EMH

Conversely, other studies have documented persistent market anomalies that contradict EMH.

The previously mentioned January and momentum effects are examples of such anomalies. Moreover, the occurrence of financial bubbles and crashes provides strong evidence against the strong form of EMH.

Efficient Market Hypothesis in Modern Finance

Despite criticisms, the EMH continues to shape modern finance in profound ways.

EMH and the Rise of Passive Investing

The EMH has been a driving force behind the rise of passive investing. If markets are efficient and all information is already priced into securities, then active management cannot consistently outperform the market.

As a result, many investors have turned to passive strategies, such as index funds and ETFs .

Impact of Technology on Market Efficiency

Advances in technology have significantly improved the speed and efficiency of information dissemination, arguably making markets more efficient. High-frequency trading and algorithmic trading are now commonplace, further reducing the possibility of beating the market.

Future of EMH in Light of Evolving Financial Markets

While the debate over market efficiency continues, the growing influence of machine learning and artificial intelligence in finance could further challenge the EMH.

These technologies have the potential to identify and exploit subtle patterns and relationships that human investors might miss, potentially leading to market inefficiencies.

The Efficient Market Hypothesis is a crucial financial theory positing that all available information is reflected in market prices, making it impossible to consistently outperform the market. It manifests in three forms, each with distinct implications.

The weak form asserts that all historical market information is accounted for in current prices, suggesting technical analysis is futile.

The semi-strong form extends this to all publicly available information, rendering both technical and fundamental analysis ineffective.

The strongest form includes even insider information, making all efforts to beat the market futile. EMH's implications are profound, affecting individual investors, portfolio managers, corporate finance decisions, and government regulations.

Despite criticisms and evidence of market inefficiencies, EMH remains a cornerstone of modern finance, shaping investment strategies and financial policies.

Efficient Market Hypothesis (EMH) FAQs

What is the efficient market hypothesis (emh), and why is it important.

The Efficient Market Hypothesis (EMH) is a theory suggesting that financial markets are perfectly efficient, meaning that all securities are fairly priced as their prices reflect all available public information. It's important because it forms the basis for many investment strategies and regulatory policies.

What are the three forms of the Efficient Market Hypothesis (EMH)?

The three forms of the EMH are the weak form, semi-strong form, and strong form. The weak form suggests that all past market prices are reflected in current prices. The semi-strong form posits that all publicly available information is instantly priced into the market. The strong form asserts that all information, both public and private, is fully reflected in stock prices.

How does the Efficient Market Hypothesis (EMH) impact individual investors and portfolio managers?

According to the EMH, consistently outperforming the market is virtually impossible because all available information is already factored into the prices of securities. Therefore, it suggests that individual investors and portfolio managers should focus on creating well-diversified portfolios that mirror the market rather than trying to beat the market.

What are some criticisms of the Efficient Market Hypothesis (EMH)?

Criticisms of the EMH often come from behavioral finance, which argues that cognitive biases can lead investors to make irrational decisions, resulting in mispriced securities. Additionally, the EMH has difficulty explaining certain market anomalies, such as the "January effect" or the "momentum effect." The occurrence of financial crises also raises questions about the validity of EMH.

How does the Efficient Market Hypothesis (EMH) influence modern finance and its future?

Despite criticisms, the EMH has profoundly shaped modern finance. It has driven the rise of passive investing and influenced the development of many financial regulations. With advances in technology, the speed and efficiency of information dissemination have increased, arguably making markets more efficient. Looking forward, the growing influence of artificial intelligence and machine learning could further challenge the EMH.

About the Author

True Tamplin, BSc, CEPF®

True Tamplin is a published author, public speaker, CEO of UpDigital, and founder of Finance Strategists.

True is a Certified Educator in Personal Finance (CEPF®), author of The Handy Financial Ratios Guide , a member of the Society for Advancing Business Editing and Writing, contributes to his financial education site, Finance Strategists, and has spoken to various financial communities such as the CFA Institute, as well as university students like his Alma mater, Biola University , where he received a bachelor of science in business and data analytics.

To learn more about True, visit his personal website or view his author profiles on Amazon , Nasdaq and Forbes .

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• Corporate Finance

Accounting Practice: Definition, Methods, and Principles

Investopedia / Matthew Collins

What Is Accounting Practice?

Accounting practice is the process and activity of recording the day-to-day financial operations of a business entity. Accounting practice is necessary to produce the legally required annual financial statements of a company. There are different accounting methods that companies can choose to use, and there are principles that companies must abide by. Generally accepted accounting principles  (GAAP) refer to a common set of accounting principles, standards, and procedures issued by the  Financial Accounting Standards Board  (FASB). Public companies in the United States must follow GAAP when their accountants compile their financial statements. Changes to the way a business compiles and reports its financials can be time consuming and costly.

Key Takeaways:

• Accounting practice is the recording of the day-to-day financial operations of a business entity necessary to produce the legally required financial statements.
• Public companies in the United States must follow GAAP in their accounting practice.
• Two popular accounting methods are cash accounting and accrual accounting.

Understanding Accounting Practices

Accounting practice is necessary so that a company can produce the annual and legally required financial statements. Business financial statements include:

• The income statement
• The comprehensive income statement
• The balance sheet
• The statement of cash flows
• The statement of stockholders equity

Historical accounting postulates form the standardized basis of an accounting practice. Companies use various accounting methods, the two primary methods being cash accounting and accrual accounting.

Cash Accounting

For cash accounting , revenue and expenses are recorded as they are received and paid, and transactions are only recorded when cash is spent or received. For example, in cash accounting, a sale is recorded when the payment is received, and an expense is recorded only when a bill is paid. This method is the most typically used method for small businesses. However, if a business generates over $5 million in sales for the year, it must choose the accrual accounting method, according to the Internal Revenue Service .

Accrual Accounting

Accrual accounting is based on the matching principle, which is intended to match the timing of the realization of revenues and an expense. By matching revenues with expenses, the accrual method gives a more accurate picture of a company's true financial position. Under the accrual method, transactions are recorded when they are incurred rather than when payment is actually made. This means a purchase order is recorded as a revenue even though the funds are not received immediately. The same goes for expenses in that they are recorded when the payment may not yet have been made.

Accounting Principles

Accounting principles are rules and concepts applied to accounting activities. GAAP refers to a common set of accounting principles, standards, and procedures issued by the  Financial Accounting Standards Board  (FASB). Public companies in the United States must follow GAAP principles when compiling their financial statements. GAAP is a combination of authoritative standards (set by policy boards) and the commonly accepted ways of recording and reporting accounting information. GAAP aims to improve the clarity, consistency, and comparability of financial information. Some examples of GAAP principles are the following:

The Revenue Recognition Principle

This principle applies to the revenue entered on the income statement. Revenue is the gross inflow of cash and receivables of an enterprise from the sale of goods of services or the yielding of any interest, royalties , and dividends .

Historical Cost Principle

The historical cost principle requires that the price paid for an asset at the time of its acquisition is the basis for its treatment in subsequent accounting periods. If the asset is acquired at no cost, the item will not be recorded as an asset. For example, a company's reputation is a valuable asset, but it is not recorded in the accounts.

Matching Principle

The matching principle requires that a company report an expense on its income statement for the period in which the related revenues are earned. Additionally, a liability should be entered on the balance sheet for the end of the accounting period. The matching principle is associated with the accrual method of accounting and it requires entry adjustments.

Full Disclosure Principle

According to this principle, the financial statements should convey information and not conceal it. Financial statements should disclose all relevant information. Because of the principle of full disclosure , companies append notes to their financial statements.

Objectivity Principle

According to the objectivity principle, the accounting data should be definite, verifiable, and free from the personal bias of the accountant. Each transaction recorded in the accounts should have evidence to support it, for example, in the form of receipts, cash memos, or invoices.

Special Considerations for Accounting Practice

As the physical and digital worlds have integrated over time, today's accounting information systems are typically computer-based methods with special accounting software.

Accounting practices and their attached systems produce financial reports used internally by management to assess performance and for strategic planning. Financial reports are also used by external stakeholders including investors, creditors, and tax authorities. When paired with accounting practices, accounting information systems support all accounting functions and activities including auditing, financial accounting and reporting, and tax management and accounting. 

Accounting practice culture often sets individual standards, behaviors, and attitudes. These ways of doing business can manifest into good and bad norms on aggregate. In the worst cases, accounting practice can lead to financial scandals. High profile scandals include Enron in 2001; Sunbeam, WorldCom , and Tyco in 2002; and Toshiba in 2015 .

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