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Nonstatistical Sampling

Nonstatistical sampling does not involve the use of statistical calculations. It relies on the subjective sampling selections by the auditor and has less of a standardized approach. Nonstatistical sampling is beneficial where:

- The auditor needs to employ professional subjective judgment.

- There is a unique issue where there is a need for a less rigid standardized approach.

- The auditor should not be restricted to explicit numbers as to materiality or risk.

In order to effectively perform nonstatistical sampling, the auditor must have a good knowledge of the data set. Knowing the contents of the data or population allows for a supportable sample selection choice and also supports the conclusion of the results. Sample selection may be based on random sampling or other nonmathematical techniques such as judgmental, haphazard, or block selection.

Judgmental selection is frequently used when the auditor is very experienced and selects samples based on sound judgment. Typically, the auditor will make the selections based on a combination of representativeness of the population, value of the items, and relative risk of the items.

Haphazard selection is where the auditor picks items without basis of any mathematical formula. The auditor believes that the items selected are representative of the population and no intentional bias was applied to any of the included or excluded items.

Block selection is where a contiguous sequence of items is selected as samples. These blocks may be invoice numbers from 1000 to 1100 or a specific type of transactions for the month of March. Block selection effectiveness can be much improved by sampling several blocks.

Sampling Risk

The sample selected either through statistical sampling or nonstatistical sampling methods might not truly reflect the population even if done with the utmost care. This is the cause for sampling risk, where the auditor's conclusion based on the selected sample may differ from the reality of the conditions of the entire population of the data set. Sampling risk occurs due to limited time and resources that prevent an audit of the entire population.

Alpha or Type I risk is the risk of incorrect rejection. That is, the auditor incorrectly concludes from the sample that the population errors are worse than they actually are.

Beta or Type II risk is the risk of incorrect acceptance. That is, the auditor incorrectly concludes from the sample that the errors in the population are better than they really are.

Auditors are usually more interested in beta (Type II) risks, as they are concerned about the failure to detect material misstatements.

 
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