The sample size depends on the risk of the sample accepted by the auditor. The selection of adequate methods in relation to the real situation, representing a solution that helps minimize the risk of sampling.
In this article we will discuss the statistical sampling method MUS. This method is also called probability-proportional sampling. This method has been used for many years and is widely accepted among auditors. Many auditors use MUS, using basically the same methods that were used before the invention of PC and software.
Monetary Unit Sampling is a sampling method that is used to determine whether account balances or cash equivalents collectively contain any misstatement. Each monetary equivalent is considered a sampling unit, so that residuals in an array with a higher value have a proportionately higher probability of selection.
The advantage of the method is that the efforts of the auditor are directed to amounts with higher values. The questions that need to be established before using this method are: population size, confidence interval, margin of error, accuracy, extrapolated errors, probability of errors.
The confidence interval is determined by coefficients that establish the sample size and the indirect scatter of detailed tests. In order to provide 95% reasonable assurance that the financial indicators do not contain significant deviations. The margin of error will be based on previous experience and will be led to establish the sample size. You should also evaluate the maximum possible error estimated in the array as a result of detailed testing of the sample.
If the extrapolated errors exceed the materiality threshold, then the auditors should extend the testing in order to check for the presence of significant errors and, in the end, make an appropriate conclusion. The probability of error is constant with respect to the element in the sample.
Using Caseware IDEA software, you can calculate the coefficients and form MUS in two clicks, then IDEA will generate a conclusion report to further examine the nature of the data. Explore and predict data with Caseware IDEA!
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