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 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 samplingas 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 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 the population is all 250 employees and the sample is random because each employee has an equal chance of being chosen 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 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 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 samplingbusinesses 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 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 2 audit sampling is necessary when the population the account transaction information is large 