Sequential Audit Sampling with Statistical Guarantees

2026-04-07

AI Summary

This study addresses the lack of rigorous statistical guarantees in sequential sampling for auditing by formulating it as a sequential hypothesis test under sampling without replacement from a finite population. It defines null and alternative hypotheses based on a tolerable deviation rate and constructs exact stopping and decision rules that provide a priori control over both Type I and Type II error probabilities. The work introduces the first sequential audit sampling framework supporting one-sided, two-stage, and truncated designs. Exact boundaries are derived using finite-population error probabilities and efficiently calibrated via Monte Carlo simulation under the least favorable deviation rate. This approach not only ensures pre-specified error control but also accurately estimates expected sample sizes, making it suitable for attribute sampling and tests of controls.

Abstract

Financial statement auditing is conducted under a risk-based evidence approach to obtain reasonable assurance. In practice, auditors often perform additional sampling or related procedures when an initial sample does not provide a sufficient basis for a conclusion. Across jurisdictions, current standards and practice manuals acknowledge such extensions, while the statistical design of sequential audit procedures has not been fully explored. This study formulates audit sampling with additional, sequentially collected items as a sequential testing problem for a finite population under sampling without replacement. We define null and alternative hypotheses in terms of a tolerable deviation rate, specify stopping and decision rules, and formulate exact sequential boundary conditions in terms of finite-population error probabilities. For practical implementation, we calibrate those boundaries by Monte Carlo simulation at least-favorable deviation rates. The exact design yields ex ante control of decision error probabilities, and the simulation-based implementation approximates that design while allowing the computation of expected stopping times. The framework is most naturally suited to attribute auditing and deviation-rate auditing, especially tests of controls, and it can be extended to one-sided, two-stage, and truncated designs.

Problem

• sequential audit sampling
• statistical guarantees
• finite population
• deviation rate
• audit risk

Innovation

• sequential audit sampling
• statistical guarantees
• finite population
• Monte Carlo calibration
• tolerable deviation rate