AUD 689 Audit Sampling Notes

Audit Sampling Overview

  • Audit sampling involves applying audit procedures to less than 100% of items, ensuring each sampling unit has an equal chance of selection (ISA 530).
  • It can be statistical or non-statistical.

Audit Evidence (ISA 500)

  • Auditors need sufficient, appropriate audit evidence to form audit opinions, often using sampling.

Sampling Risk

  • Sampling risk is the risk that the auditor's conclusion based on a sample differs from the conclusion from a 100% examination.
  • Auditors can lower sampling risk by increasing the sample size.

Types of Sampling Risk

  • Compliance/Control Testing:
    • Type 1: Assessing control risk too high (incorrectly concluding high risk).
    • Type 2: Assessing control risk too low (incorrectly concluding low risk).
  • Substantive Testing:
    • Type 1: Risk of concluding material error exists when it doesn't.
    • Type 2: Risk of concluding material error doesn't exist when it does.

Non-Sampling Risk

  • Non-sampling risk arises from factors other than sample size (e.g., misinterpreting evidence).
  • Minimized through proper planning, supervision, and quality control.

Types of Audit Sampling

  • Statistical (Probabilistic) Sampling
  • Non-Statistical (Non-Probabilistic or Judgmental) Sampling

Statistical Sampling

  • Involves random sample selection and probability theory to evaluate results and measure sampling risk.
  • Allows reliance on smaller samples and systematic approach

Non-Statistical Sampling

  • Relies on auditor's professional judgment for sample selection and evaluation.
  • More subjectivity in sampling decisions.

Categories of Statistical Sampling

  • Attribute Sampling
  • Monetary Unit Sampling
  • Classical Variable Sampling

Attribute Sampling

  • Estimates the proportion of a population with a specific characteristic (e.g., deviation rate in controls).
  • Example: Credit check performed or not

Monetary Unit Sampling

  • Uses attribute sampling to estimate misstatement amount.
  • Suited for detecting overstatement errors, especially with low error rates.
  • Expresses conclusions in monetary amounts.
  • E.g., auditing accounts receivable, loans, investment securities and inventory

Classical Variable Sampling

  • Uses normal distribution theory.
  • Estimates amount for transactions or account balances and determine material misstatements.
  • E.g., auditing account receivable in which a large amount of misstatement is expected, and inventory

When to Use Statistical Sampling

  • When accounting/internal control systems are reliable/effective
  • Large/Homogeneous populations
  • Identifiable/Accessible items

Advantages of Statistical Sampling

  • Efficient sample design
  • Measures sufficiency of evidence
  • Quantifies sampling risk
  • Smaller Sample Size
  • Systematic and Scientific Approach

Disadvantages of Statistical Sampling

  • Additional costs include auditor training and software

Non-Statistical Sampling

  • Less costly and time-consuming.

Advantages of Non-Statistical Sampling

  • Lower training costs, ease of implementation, proposed adjustment based on quantitative analysis

Disadvantages of Non-Statistical Sampling

  • Training difficulties, absence of consistency and uniformity, potential litigation, may cause incorrect evaluation of sampling risk

Similarities Between Statistical and Non-Statistical Sampling

  • Sample design, sample selection, performing audit procedures, and evaluating results.

Differences Between Statistical and Non-Statistical Sampling

  • Statistical sampling relies on statistical formulas while non-statistical relies on experience.
  • Statistical sampling is objective; non-statistical is subjective.
  • Different auditors may have different non-statistical approaches.
  • Statistical sampling can delegate the responsibility to lower rank staff while non-statistical need senior audit staff.
  • Statistical sampling is defensible in court of law

Application of Sampling on Audit Tests

  1. Planning the sample and design the sample size
  2. Selecting the sample
  3. Testing the sample (Vouching)
  4. Evaluating the results

1) Planning the Sample

  • Consider audit procedure objectives and population attributes.
  • Population is the entire data set from which the sample is selected.
Steps in Planning the Sample
  • Determine test objectives.
  • Define errors or deviations sought.
  • Identify population and sampling unit.
  • Stratify population.
  • Decide sample size.
Stratification of Population
  • Dividing a population into subpopulations with similar characteristics such as Monetary value
Factors Influencing Sample Size
  • Test of Control: Auditor's intended reliance on internal control, tolerable error rate, expected error rate, and confident level needed.
  • Substantive Procedures: Assessment of inherent and control risk, use of other substantive procedures, confident level needed, tolerable error, and expected error rate.

2) Selecting the Sample

  • Samples should represent the population, with all items having an equal chance of selection.
  • Statistical requires random selection; non-statistical uses auditor judgment.
Principal Methods of Selecting Samples
  • Random Sampling
  • Systematic Selection
  • Haphazard Selection
Random Sampling
  • Each item has an equal chance of selection; computer or numbers tables used.
  • Advantage: No auditor bias.
Systematic Selection
  • Sampling interval determined by dividing population by sample size. A starting number is selected in the first interval and then every nthn^{th} item is selected
Haphazard Sample
  • Selection without specific reason or structured technique; avoid bias.
  • Useful for non-statistical sampling, but auditor tend to bias.

3) Testing the Sample

  • Perform appropriate audit procedures on each item selected.
  • Replace inappropriate samples (e.g., voided transactions).
  • Perform alternative tests if unable to apply planned procedures (e.g., missing invoices).

4) Evaluating the Results

  • Examine implications of each error discovered by analyzing its nature and cause.
  • Consider qualitative aspects such as disclosure requirements and potential fraud.
  • Project error to population and compare with acceptable error.
Draw Final Conclusion
  • Evaluate sample result and determine whether assessment of population is confirmed or needs to be revised.
  • If errors indicate ineffective control or material misstatement:
    • Request management investigate and adjust.
    • Modify further audit procedures.
    • Consider effect on the audit report.