Double-checking auditors: a Bayesian approach

Abstract

Summary. The paper discusses the problem of a fallible auditor who may classify incorrect values as ‘correct’, or vice versa. To detect these mistakes, a sample of the auditor's classifications is checked again, now by an infallible expert. From the classifications of both the auditor and the expert the error rate in the population is estimated. We show that classical confidence intervals for the error rate are of limited practical use. Instead, we propose and implement a Bayesian approach.