A Note on Optimal Sample Sizes in Compliance Tests Using a Formal Bayesian Decision-Theoretic Approach for Finite and Infinite Populations.

In this paper we examine optimal sample sizes using a formal Bayesian decision-theoretic approach, in which auditors seek to maximize expected utility subject to a budgetary constraint. The results are presented for finite and infinite population models based on a linear loss function and the prior distribution mean error rates for audit populations taken from the GA study. As with GA, the sampling designs are for compliance tests. Our results indicate that the formal Bayesian approach can provide optimal sample sizes which differ from those found using the informal approach, and that the optimal sample sizes may be larger for the finite model. [ABSTRACT FROM AUTHOR]