The internal auditor estimates the amount of error in routine accounting records by sampling and other procedures. These investigations cost money, and there is a risk that his conclusions may be wrong. This article shows how Bayesian theory can be used in planning and controlling the routine audit, and in interpreting audit findings. It suggests rules for choosing economic sample sizes in discovery sampling, and deciding whether to submit an unfavourable report on an accounting system when unexpected errors are found. It also discusses the use which the practitioner should make of any prior knowledge of a system which he may possess, and the impact of such knowledge on the frequency of audits. This study is seen as part of the current search for a general theory of audit decision making, and suggestions are made for further empirical and theoretical work in that field.
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