n this session I discuss attribute Sampling.
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Define and describe audit sampling for exception rates.
Auditors use sampling for tests of controls and substantive tests of transactions to determine whether controls are operating effectively and whether the rate of monetary errors is below tolerable limits. To do this, auditors estimate the percent of items in a population containing a characteristic or attribute of interest. This percent is called the occurrence rate or exception rate. For example, if an auditor determines that the exception rate for the internal verification of sales invoices is approximately 3 percent, then on average 3 of every 100 invoices are not properly verified.

Auditors are interested in the following types of exceptions in populations of accounting data:

Deviations from the client’s established controls
Monetary misstatements in populations of transaction data
Monetary misstatements in populations of account balance details
Knowing the exception rate is particularly helpful for the first two types of exceptions, which involve transactions. Therefore, auditors make extensive use of audit sampling that measures the exception rate in doing tests of controls and substantive tests of transactions. With the third type of exception, auditors usually need to estimate the total dollar amount of the exceptions because they must decide whether the misstatements are material. When auditors want to know the total amount of a misstatement, they use methods that measure dollars, not the exception rate.

The exception rate in a sample is used to estimate the exception rate in the entire population, meaning it is the auditor’s “best estimate” of the population exception rate. The term exception should be understood to refer to both deviations from the client’s control procedures and amounts that are not monetarily correct, whether because of an unintentional accounting error or any other cause. The term deviation refers specifically to a departure from prescribed controls.

Assume, for example, that the auditor wants to determine the percentage of duplicate sales invoices that do not have shipping documents attached. Because the auditor cannot check every invoice, the actual percentage of missing shipping documents remains unknown. The auditor obtains a sample of duplicate sales invoices and determines the percentage of the invoices that do not have shipping documents attached. The auditor then concludes that the sample exception rate is the best estimate of the population exception rate.

Because the exception rate is based on a sample, there is a significant likelihood that the sample exception rate differs from the actual population exception rate. This difference is called the sampling error. The auditor is concerned with both the estimate of the sampling error and the reliability of that estimate, called sampling risk. Assume the auditor determines a 3 percent sample exception rate, and a sampling error of 1 percent, with a sampling risk of 10 percent. The auditor can state that the interval estimate of the population exception rate is between 2 percent and 4 percent (3 percent ± 1) with a 10 percent risk of being wrong (and a 90 percent chance of being right).

In using audit sampling for exception rates, the auditor wants to know the most the exception rate is likely to be, rather than the width of the confidence interval. So, the auditor focuses on the upper limit of the interval estimate, which is called the estimated or computed upper exception rate (CUER) in tests of controls and substantive tests of transactions. Using figures from the preceding example, an auditor might conclude that the CUER for missing shipping documents is 4 percent at a 5 percent sampling risk, meaning the auditor concludes that the exception rate in the population is no greater than 4 percent with a 5 percent risk of the exception rate exceeding 4 percent. Once it is calculated, the auditor can consider CUER in the context of specific audit objectives. If testing for missing shipping documents, for example, the auditor must determine whether a 4 percent exception rate indicates an acceptable control risk for the occurrence objective.

Tolerable exception rate, TER, TDR, Tolerable deviation rate, Acceptable risk of overreliance, ARO, Estimated population exception rate, EPER, Exception deviation rate, Exception rate, Computed upper exception rate, CUER, Upper deviation rate, sampling risk, over reliance on internal control, audit risk model

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