Monetary unit sampling: Improving estimation of the total audit error

In the practice of auditing, for cost concerns, auditors verify only a sample of accounts to estimate the error of the total population of accounts. The most common statistical method to select an audit sample is by monetary unit sampling (MUS). However, common MUS estimation practice does not explicitly recognize the multiple distributions within the population of account errors. This often leads to excessive conservatism in auditors' judgment of population error. In this paper, we review the common MUS estimation practice, and introduce our own method which uses the Zero-Inflation Poisson (ZIP) distribution to consider zero versus non-zero errors explicitly. We argue that our method is better suited to handle the real populations of account errors, and show that our ZIP upper bound is both reliable and efficient for MUS estimation of accounting data.