What Is Sampling?
Sampling is a process in statistical analysis where researchers take a predetermined number of observations from a larger population. Sampling allows researchers to conduct studies about a large group by using a small portion of the population. The method of sampling depends on the type of analysis being performed, but it may include simple random sampling or systematic sampling. Sampling is commonly done in statistics, psychology, and the financial industry.
Key Takeaways
- Sampling allows researchers to use a small group from a larger population to make observations and determinations.
- Types of sampling include random sampling, block sampling, judgment sampling, and systematic sampling.
- Researchers should be aware of sampling errors, which may be the result of random sampling or bias.
- Companies use sampling as a marketing tool to identify the needs and wants of their target market.
- Certified public accountants use sampling during audits to determine the accuracy and completeness of account balances.
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How Sampling Works
It can be difficult for researchers to conduct accurate studies on large populations. In some cases, it can be impossible to study every individual in the group. That's why they often choose a small portion to represent the entire group. This is called a sample. Samples allow researchers to use characteristics of the small group to make estimates of the larger population.
The chosen sample should be a fair representation of the entire population. When taking a sample from a larger population, it is important to consider how the sample is chosen. To get a representative sample, it must be drawn randomly and encompass the whole population. For example, a lottery system could be used to determine the average age of students in a university by sampling 10% of the student body.
Sampling is commonly used when studying large portions of the population for economic purposes. For instance, the monthly employment report involves the use of sampling, the U.S. Bureau of Labor Statistics (BLS) reports:
- The Current Employment Statistics by using 122,000 businesses and government agencies
- The Current Population Survey with a sample of 60,000 different households across the country
Researchers should be aware of sampling errors. This occurs when the sample that is selected doesn't represent the entire population. This means that the results taken from the sample deviate from the larger population. Sampling error may occur randomly or because there is some form of bias. For instance, some members of the sample group may choose not to participate, or they differ in some way from other participants.
Sampling isn't an exact science, so the results should be taken as generalizations. As such, don't make conclusions about the broader population based on the sample group.
Types of Audit Sampling
As noted above, there are several different types of sampling that researchers can use. These include random, judgment, block, and systemic sampling. These are discussed in more detail below.
Random Sampling
With random sampling, every item within a population has an equal probability of being chosen. It is the furthest removed from any potential bias because there is no human judgement involved in selecting the sample.
For example, a random sample may include choosing the names of 25 employees out of a hat in a company of 250 employees. Thepopulationis all 250 employees, and the sample is random because each employee has an equal chance of being chosen.
Judgment Sampling
Auditor judgment may be used to select the sample from the full population. An auditor may only be concerned about transactions of a material nature. For example, assume the auditor sets the threshold for materiality for accounts payable transactions at $10,000. If the client provides a complete list of 15 transactions over $10,000, the auditor may just choose to review all transactions due to the small population size.
The auditor may alternatively identify all general ledger accounts with a variance greater than 10% from the prior period. In this case, the auditor is limiting the population from which the sample selection is being derived. Unfortunately, human judgment used in sampling always comes with the potential for bias, whether explicit or implicit.
Block Sampling
Block sampling takes a consecutive series of items within the population to use as the sample. For example, a list of all sales transactions in an accounting period could be sorted in various ways, including by date or by dollar amount.
An auditor may request that the company's accountant provide the list in one format or the other in order to select a sample from a specific segment of the list. This method requires very little modification on the auditor's part, but it is likely that a block of transactions will not be representative of the full population.
Systematic Sampling
Systematic sampling begins at a random starting point within the population and uses a fixed, periodic interval to select items for a sample. The sampling interval is calculated as the population size divided by the sample size. Despite the sample population being selected in advance, systematic sampling is still considered random if the periodic interval is determined beforehand and the starting point is random.
Assume that an auditor reviews the internal controls related to a company's cash account and wants to test the company policy that stipulates that checks exceeding $10,000 must be signed by two people. The population consists of every company check exceeding $10,000 during the fiscal year, which, in this example, was 300. The auditor uses probability statistics and determines that the sample size should be 20% of the population or 60 checks. The sampling interval is 5 or 300 checks ÷ 60 sample checks.
Therefore, the auditor selects every fifth check for testing.Assuming no errors are found in the sampling test work, the statistical analysis gives the auditor a 95% confidence rate that the check procedure was performed correctly. The auditor tests the sample of 60 checks and finds no errors, so he concludes that the internal control over cash is working properly.
Example of Sampling
Market Sampling
Businesses aim to sell their products and/or services to target markets.Before presenting products to the market, companies generally identify the needs and wants of their target audience. To do so, they may employ sampling of the target market population to gain a better understanding of those needs to later create a product and/or service that meets those needs.In this case, gathering the opinions of the sample helps to identify the needs of the whole.
Audit Sampling
During a financial audit, a certified public accountant (CPA) may use sampling to determine the accuracy and completeness of account balances in their client's financial statements. This is called audit sampling. Audit sampling is necessary when the population (the account transaction information) is large.
What Is Sampling Error?
Sampling error is what happens when the sample collected for review doesn't represent the entire population being studied. This jeopardizes the accuracy and validity of the study being conducted. For instance, sampling error occurs if researchers include professors in the sample when they're trying to determine how students feel about the university experience. Sampling error may be random or the result of some type of bias.
What Is Cluster Sampling?
Cluster sampling is a form of probability sampling. When researchers conduct cluster sampling, they divide the population into smaller groups. They then select individuals randomly from these groups to form their samples and conduct their studies. This kind of sampling is used when both the overall population and sample size is too large to handle.
What's the Difference Between Probability and Non-Probability Sampling?
Probability sampling gives researchers the chance to come to stronger conclusions about the entire population that is being studied. It involves the use of random sampling, which means that all of the participants in the group are equally likely to get a chance to be chosen as a representative sample of the entire population. The result is often unbiased.
Non-probability sampling, on the other hand, allows researchers to easily collect information. This type of sampling is generally biased as it is unknown which participants will be chosen as a sample.
The Bottom Line
Statisticians often resort to sampling in order to conduct research when they're dealing with large populations. Sampling is a technique that involves taking a small number of participants from a much bigger group. This is often found when data needs to be collected about the population, including statistical analysis, population surveys, and economic studies.
