11.2 Designing and Executing Audit Samples
5 min read•august 13, 2024
Audit sampling is a crucial technique in auditing, allowing auditors to draw conclusions about entire populations by examining only a portion. This section explores the design and execution of audit samples, covering objectives, determination, selection methods, and procedures for sample items.
Proper sampling helps auditors balance efficiency with risk management. We'll learn how to select representative samples, apply appropriate audit procedures, and evaluate results. Understanding these concepts is key to conducting effective audits and forming reliable conclusions about financial statements.
Audit Sample Objectives and Scope
Defining Audit Sampling and Its Objectives
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- Audit sampling applies audit procedures to less than 100% of the of transactions or account balances to obtain evidence and draw conclusions about the entire population
- Audit sampling objectives:
- Obtain sufficient appropriate evidence
- Reduce to an acceptable level
- Apply audit procedures to the sample to form a conclusion about the population
- The scope of an audit sample is determined by:
- Specific audit objectives
- Population size and characteristics
- Sampling method used
Understanding Sampling and Non-Sampling Risk
- Sampling risk is the possibility that the auditor's conclusion based on a sample may be different from the conclusion if the entire population were subjected to the same audit procedure
- Non-sampling risk can also impact the auditor's conclusion which arises from factors that cause the auditor to reach an erroneous conclusion for any reason not related to sample size (auditor bias, misinterpretation of evidence)
Sample Size Determination
Factors Influencing Sample Size
- Sample size is influenced by:
- Auditor's assessment of the risks of material misstatement
- (maximum error in the population that the auditor is willing to accept and still conclude that the result from the sample has achieved the audit objective, typically set at less than performance materiality)
- Expected misstatement (amount of misstatement the auditor expects in the population, higher expected misstatement will require a larger sample size)
- Assurance needed from substantive procedures
- The auditor may use statistical or approaches
- allows the auditor to measure sampling risk and provides a more objective basis for determining sample size
Balancing Sampling Risk and Audit Efficiency
- Increasing the sample size reduces sampling risk but increases audit effort and cost
- The auditor must balance the level of sampling risk with audit efficiency (cost-benefit considerations)
- Factors to consider:
- Significance of the audit area
- Complexity of transactions
- Effectiveness of internal controls
- Prior audit experience
Representative Sample Selection
Sampling Techniques for Representative Samples
- The sample should be representative of the population, such that it has approximately the same characteristics as the population from which it was selected
- Simple random sampling selects sample items such that each sampling unit has an equal chance of selection (requires homogeneous sampling units)
- Systematic selection involves selecting items using a constant interval between selections, with the first interval having a random start (risk of bias if population is structured in a pattern matching the sampling interval)
- Monetary Unit Sampling (MUS) is a form of value-weighted selection in which sample size, selection and evaluation results in a conclusion in monetary amounts (each individual monetary unit, such as a dollar, has an equal chance of selection)
Non-Statistical Sampling Techniques
- Haphazard selection is a non-statistical technique in which the auditor selects the sample without following a structured technique, avoiding any conscious bias or predictability (cannot be used to project misstatements to the population)
- Block selection involves selecting contiguous items from within the population (cannot be used to draw statistical inferences about the entire population)
- These techniques may be appropriate for small populations or when the auditor's professional judgment determines that statistical sampling is not necessary
Sample Item Audit Procedures
Applying Appropriate Audit Procedures
- The auditor should perform audit procedures that are appropriate to the particular audit objective on each sample item
- For tests of controls, the auditor should:
- Inspect documents
- Observe processes
- Re-perform client procedures on the sampled items to determine if the controls are operating effectively
- For substantive procedures, the auditor applies appropriate procedures to each sample item to determine if a misstatement exists and its quantitative impact, which may involve:
- Tracing to supporting documents
- External confirmation
- Recalculation
Handling Inappropriate Items and Misstatements
- If a selected item is not appropriate for the application of the designed audit procedure, it may be replaced with another item using an unbiased selection process
- Misstatements or deviations identified in the sample must be investigated to determine:
- Their nature and cause
- Their potential effect on the purpose of the audit procedure
- Their potential effect on other areas of the audit
- The auditor should consider the qualitative aspects of misstatements (indicators of fraud, misstatements that may indicate a significant deficiency or material weakness in internal control)
Sampling Process Documentation
Required Documentation Elements
- The audit documentation should include:
- Sampling objectives
- Definition of the population and sampling unit
- Sample size
- Method of sample selection
- Listing of items tested
- Misstatements or deviations identified
- Conclusions reached
- For statistical samples, the documentation should also include:
- Risk of incorrect acceptance
- Tolerable misstatement
- Expected misstatement
- Population size
- Sampling interval and starting point (for systematic selection)
Audit Software and Working Paper Considerations
- The working papers should be sufficient for an experienced auditor, with no prior connection to the audit, to understand the nature, timing, extent and results of the procedures performed, evidence obtained and conclusions reached
- If audit software is used, relevant inputs and outputs should be retained as part of the audit documentation (screenshots, exported reports)
- The auditor should ensure the completeness and accuracy of any system-generated reports or data used in the sampling process
Evaluating Sample Results
- Evaluation of sample results involves:
- Determining whether the initial assessment of relevant risks remain appropriate
- Determining whether additional testing is needed
- Determining whether misstatements are material, individually or in aggregate
- The auditor should consider the nature and cause of any misstatements detected and their possible effect on other audit areas or financial statement assertions
- If the sample results suggest a previously unidentified risk of material misstatement, the auditor should revise the audit plan accordingly
Key Terms to Review (18)
Attribute sampling: Attribute sampling is a statistical method used in auditing to determine the presence or absence of a specific characteristic or attribute within a sample of items. This technique allows auditors to evaluate the effectiveness of controls or processes by estimating the rate of occurrence of a certain attribute in a larger population, making it crucial for assessing compliance and operational effectiveness.
Audit evidence: Audit evidence refers to the information collected by an auditor to draw conclusions about the fairness and accuracy of a client's financial statements. This evidence can be obtained through various methods, including tests of transactions, analytical procedures, and direct observations. The quality and quantity of the audit evidence are critical in supporting the auditor's opinion on the financial statements.
Confidence Level: Confidence level refers to the probability that a statistical sample accurately represents the population from which it is drawn. This concept plays a crucial role in sampling methods, as it determines the degree of certainty an auditor has that their sample results reflect the true characteristics of the entire population. A higher confidence level indicates a greater assurance that the sample findings are reliable, impacting how samples are designed, executed, and evaluated.
Expected misstatement: Expected misstatement refers to the auditor's estimate of the amount or percentage of misstatements in financial statements based on prior experience or analysis of the population being audited. This concept is crucial as it helps auditors assess the risk of material misstatement and determine the appropriate sample size for testing. It connects to planning the audit procedures and allows auditors to make informed decisions about the nature, timing, and extent of testing required.
Generally Accepted Auditing Standards (GAAS): Generally Accepted Auditing Standards (GAAS) are a set of guidelines established to ensure the quality and consistency of audit engagements. These standards provide a framework for auditors to follow during the planning, execution, and reporting phases of an audit, helping to safeguard the integrity of financial statements and increase stakeholder trust.
International Standards on Auditing (ISA): International Standards on Auditing (ISA) are guidelines established by the International Auditing and Assurance Standards Board (IAASB) that provide a framework for auditing financial statements. These standards aim to enhance the consistency and quality of audits across different jurisdictions, ensuring that auditors conduct their work with a high level of professionalism and integrity.
Judgmental sampling: Judgmental sampling is a non-statistical sampling method where the auditor uses their professional judgment to select specific items for testing, rather than relying on random selection. This approach allows auditors to focus on items they believe may have a higher risk of misstatement or are more representative of the population, thus enhancing the efficiency of the audit process.
Mean: In statistics, the mean is the average value of a set of numbers, calculated by summing all the values and dividing by the total number of values. This concept is essential for understanding data sets and making informed decisions based on sample data. The mean helps auditors assess the overall trends and characteristics of financial data, providing insights into the population being analyzed.
Non-Statistical Sampling: Non-statistical sampling is an audit sampling method that relies on the auditor's judgment rather than mathematical probability to select items for testing. This approach allows auditors to use their expertise and understanding of the client's business to identify specific transactions or account balances that may pose a higher risk of material misstatement. While it can be effective, this method does not provide a statistical basis for projecting results or quantifying the level of assurance obtained from the sample.
Population: In auditing, population refers to the entire set of items or data that are the subject of an audit sample. This could include transactions, account balances, or other financial records that an auditor needs to examine. Understanding the population is crucial for determining sample size and ensuring that the results from the sample can be generalized back to the whole population effectively.
Random selection: Random selection is a sampling technique where each member of a population has an equal chance of being chosen. This method helps eliminate bias in the selection process, ensuring that the sample accurately represents the entire population, which is crucial when drawing conclusions from audit samples.
Sample size: Sample size refers to the number of individual observations or items that are selected from a larger population for the purpose of statistical analysis. It plays a crucial role in determining the reliability and validity of audit results, as a well-chosen sample size can provide insights into the entire population while minimizing the risk of errors and biases.
Sampling risk: Sampling risk is the risk that an auditor's conclusion based on a sample may be different from the conclusion they would reach if they examined the entire population. This concept is crucial in audit practices, as it highlights the inherent uncertainty in making decisions based on limited information. It plays a key role in determining the effectiveness and efficiency of audit sampling methods, impacting both the design of samples and the evaluation of sample results.
Standard deviation: Standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of values. It indicates how much individual data points deviate from the mean (average) of the data set, giving insight into the consistency or variability of the data. In the context of audit samples, understanding standard deviation helps auditors assess the reliability and representativeness of the sample taken from a population.
Statistical Sampling: Statistical sampling is a method used to select and analyze a subset of items from a larger population to make inferences about that population. This technique allows auditors to draw conclusions about the entire set based on the analysis of a smaller, manageable portion, enhancing efficiency and effectiveness in gathering audit evidence.
Substantive Testing: Substantive testing refers to audit procedures designed to detect material misstatements in financial statements, focusing on the accuracy and validity of transactions and account balances. This type of testing plays a vital role in assessing the completeness and accuracy of financial information, helping auditors form an opinion on the financial statements.
Tolerable Misstatement: Tolerable misstatement refers to the maximum amount of misstatement in an account balance or class of transactions that an auditor is willing to accept while still concluding that the financial statements are free from material misstatement. This concept plays a critical role in setting materiality levels, assessing audit risk, and determining sample sizes for testing, as it helps auditors gauge the level of errors they can tolerate without impacting the overall fairness of the financial statements.
Variables sampling: Variables sampling is a statistical method used in auditing to evaluate the numerical characteristics of a population by selecting a subset of items to draw conclusions about the entire population. This technique focuses on measuring and estimating values, such as total dollar amounts or averages, which helps auditors determine whether financial statements are free from material misstatement. By using variables sampling, auditors can efficiently gather evidence to support their assessments and provide a basis for their audit conclusions.