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.

翻译：财务报表审计采用基于风险证据的方法以获取合理保证。实践中，当初始样本无法为结论提供充分依据时，审计师通常进行补充抽样或执行相关程序。在不同司法管辖区，现行准则和实务手册均承认此类扩展，但序贯审计程序的统计设计尚未得到充分探索。本研究将包含额外序贯收集项目的审计抽样问题，形式化为对有限总体进行无放回抽样的序贯检验问题。我们以可容忍偏差率定义原假设与备择假设，指定停止规则与决策规则，并立足于有限总体误差概率构建精确的序贯边界条件。为便于实际实施，我们通过在最小有利偏差率下进行蒙特卡洛模拟校准这些边界。该精确设计可实现决策误差概率的事前控制，而基于模拟的实施方法在近似该设计的同时，允许计算期望停止时间。该框架最适用于属性审计与偏差率审计（尤其是控制测试），并可扩展至单侧、两阶段及截断设计。