11.1 Statistical and Non-Statistical Sampling Methods

Auditing involves examining financial records, but checking every transaction is impractical. That's where sampling comes in. Auditors use statistical or non-statistical methods to test a subset of data and draw conclusions about the whole population.

Statistical sampling uses probability theory to determine sample size and evaluate results. Non-statistical sampling relies on auditor judgment. Both methods have pros and cons, and choosing the right approach depends on factors like population size, desired assurance level, and time constraints.

Statistical vs Non-statistical Sampling

Statistical Sampling

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• Applies probability theory to determine sample size, select the sample, and evaluate results
• Allows the auditor to measure sampling risk and project the sample results to the population within a specified confidence level
• Provides an objective and defensible basis for determining sample size and evaluating results
• Enables the auditor to make statistical inferences about the population

Non-statistical Sampling

• Relies on auditor judgment to determine sample size, select the sample, and evaluate results
• Does not allow the auditor to measure sampling risk or make statistical inferences about the population
• Allows the auditor to use professional judgment and knowledge of the client to focus on high-risk areas
• May be more appropriate for small or non-homogeneous populations

Commonalities

• Both methods involve examining less than 100% of the population
• Require the auditor to use professional judgment in planning and performing the procedures

Advantages and Limitations of Sampling

Advantages of Statistical Sampling

• Provides an objective and defensible basis for determining sample size and evaluating results
• Allows the auditor to quantify and control sampling risk (e.g., setting a confidence level of 95%)
• Enables the auditor to make statistical inferences about the population (e.g., projecting misstatements to the entire population)
• Suitable for large and homogeneous populations (e.g., testing a large number of similar transactions)

Limitations of Statistical Sampling

• Requires specialized knowledge and training in statistics
• Can be more time-consuming and costly than non-statistical sampling
• May not be feasible for small or non-homogeneous populations (e.g., testing a small number of unique transactions)
• Requires the population to be sufficiently large and homogeneous for statistical methods to be valid

Advantages of Non-statistical Sampling

• Allows the auditor to use professional judgment and knowledge of the client to focus on high-risk areas
• Can be more efficient and cost-effective than statistical sampling in certain situations (e.g., when the auditor has extensive knowledge of the client's operations)
• May be more appropriate for small or non-homogeneous populations (e.g., testing a small number of unique transactions)
• Provides flexibility in the selection of sample items based on the auditor's judgment

Limitations of Non-statistical Sampling

• Does not provide an objective basis for determining sample size or evaluating results
• Does not allow the auditor to quantify or control sampling risk
• Cannot be used to make statistical inferences about the population
• Relies heavily on the auditor's judgment, which may be subject to bias or inconsistency

Choosing the Right Sampling Method

When to Use Statistical Sampling

• The population is large and homogeneous (e.g., testing a large number of similar transactions)
• The auditor needs to quantify and control sampling risk (e.g., setting a confidence level of 95%)
• The auditor wants to make statistical inferences about the population (e.g., projecting misstatements to the entire population)
• The audit requires a high level of assurance or is subject to regulatory oversight (e.g., audits of public companies)

When to Use Non-statistical Sampling

• The population is small or non-homogeneous (e.g., testing a small number of unique transactions)
• The auditor has extensive knowledge of the client and wants to focus on high-risk areas
• The audit requires a lower level of assurance or is not subject to regulatory oversight (e.g., audits of small private companies)
• Time and cost constraints make statistical sampling impractical

Sampling Risk vs Non-sampling Risk

Sampling Risk

• The risk that the auditor's conclusion based on a sample may be different from the conclusion that would be reached if the entire population were subjected to the same audit procedure
• Can be controlled by increasing sample size, using a more representative sampling method, or setting a lower tolerable misstatement
• Examples of sampling risk:
  • Selecting a sample that is not representative of the population (selection bias)
  • Failing to detect a material misstatement that exists in the sample (detection risk)

Non-sampling Risk

• The risk that the auditor's conclusion may be incorrect for reasons other than sampling
• Can be caused by human error, misinterpretation of evidence, or use of inappropriate audit procedures
• Can be controlled through proper planning, supervision, and review of audit work, as well as the use of appropriate audit techniques and tools
• Examples of non-sampling risk:
  • Misinterpreting audit evidence (e.g., misunderstanding the terms of a contract)
  • Failing to perform a necessary audit procedure (e.g., not confirming accounts receivable)

Considering Both Risks

• Both sampling risk and non-sampling risk should be considered in determining the overall audit risk
• The auditor should evaluate the sufficiency and appropriateness of audit evidence obtained from both sampling and non-sampling procedures
• The auditor should use professional judgment to balance the costs and benefits of reducing sampling and non-sampling risk to an acceptable level