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SAMPLING

Merriam-Webster defines sampling as:

the act, process, or technique of selecting a representative part of a population for the purpose of determining parameters or characteristics of the whole population.1

Simply put, sampling is a process of selecting a subset of the population or a number of records from the data set for the purpose of making inferences or conclusions to the entire population or data set.

Audit sampling is the audit procedure of examining of a portion of items within a class of transactions in order to evaluate one or more characteristics of that entire class. Either statistical or nonstatistical sampling methods may be used against a part of the entire data set to make a conclusion regarding the entire data set.

Sampling is effective when the audit procedure or step does not require a 100 percent review of the population of the class, but a decision or conclusion is required and it is not cost effective to audit 100 percent of the transactions.

Statistical Sampling

Statistical sampling uses statistical mathematical calculations for selecting and then evaluating a sample from the data set. Statistical sampling outlines in numeric terms the parameters and precision levels associated with the sample conclusion.

One such use of statistical sampling that we are most familiar with are polls used to determine candidates' current standings in upcoming elections or in popularity surveys.

In a November 2013 poll, Toronto's mayor Rob Ford maintained his 42 percent approval rating after he "admitted he has smoked crack, bought illegal drugs, and might have driven drunk,"2 among other issues. The poll of 1,049 Toronto residents determined the 42 percent rating with accuracy results of plus or minus 3 percent, 19 times out of 20.

The 19 times out of 20 translates to a confidence level of 95 percent, so the 1,049 sample results can be applied to the general population of Toronto with a confidence level of 95 percent that the approval rating is between 39 percent and 45 percent.

An auditor may be verifying an account balance through statistical sampling and conclude that it is $100,000 plus or minus 5 percent or $5,000 each way ($95,000 to $105,000), 19 times out of 20. The conclusion would be that given the precision of the sample at 5 percent (plus or minus $5,000), there is assurance that the balance is correct with a confidence level of 95 percent. In addition, if the materiality was predetermined to be at 7 percent, then it can be concluded that there would no material error based on the precision level.

Confidence level is the remaining factor when the acceptable sampling risk is eliminated. In the example of selecting a 95 percent confidence level, you allow only a 5 percent chance of getting the wrong sample that does not adequately represent the entire population.

Using statistical samples to obtain familiarity with the data set might be useful but if it is not used to reach a conclusion, it cannot be considered as part of the audit procedure. In addition to formulating a conclusion, statistical sampling must use statistical calculations, and the sample must be random.

Proper use of statistical sampling is beneficial because it:

- Requires a scientifically accepted and defined approach.

- Allows the auditor to maintain professional judgment in regard to audit risks and materiality.

- Displays the sample results and conclusion in relation to the selected data set population along with defied judgment selections.

 
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