Enhancing Efficiency and Flexibility in Audits through Bayesian Optional Stopping

To verify the accuracy of organizations’ financial statements, auditors collect and evaluate audit evidence. Often, it is too time-consuming or expensive to analyze the entire population of audit evidence. In such cases, auditors may use statistical sampling to evaluate a sample of the evidence and extrapolate the findings to the rest of the population. When statistically extrapolating the findings from the sample to the population, auditors can choose between the frequentist or Bayesian statistical paradigm. The two paradigms differ on various aspects, but one crucial difference is that the frequentist paradigm requires the auditor to determine the sample size before collecting any data, whereas the Bayesian paradigm does not. Consequently, auditors employing a Bayesian method can monitor evidence as it comes in and stop whenever sufficient evidence has been gathered. This practice, also known as Bayesian optional stopping, enhances efficiency and flexibility in audits.