SUMMARY Statistical methods play an important role in auditors’ analyses of their clients’ data. A key component of the statistical approach to auditing is assessing the strength of evidence for or against a hypothesis. We argue that the frequentist statistical methods often used by auditors cannot provide the statistical evidence that audit standards advocate. In this article, we discuss an alternative approach that can provide this evidence: Bayesian inference. First, we explore the philosophical differences between frequentist and Bayesian inference. Second, we discuss misconceptions in the interpretation of frequentist statistical evidence. Finally, we show (as an alternative to the frequentist p-value) how the Bayes factor allows the auditor to obtain and interpret statistical evidence in line with audit standards. Thus, we contribute to audit theory and practice by showing how Bayesian inference can quantify audit evidence. Data Availability: The data supporting the findings in this article are available in the OSF repository at https://doi.org/10.17605/OSF.IO/WTN9G.1