Evaluation of Procedures for Adjusting Problem-Discovery Rates Estimated From Small Samples

Abstract

There are 2 excellent reasons to compute usability problem-discovery rates. First, an estimate of the problem-discovery rate is a key component for projecting the required sample size for a usability study. Second, practitioners can use this estimate to calculate the proportion of discovered problems for a given sample size. Unfortunately, small-sample estimates of the problem-discovery rate suffer from a serious overestimation bias. This bias can lead to serious underestimation of required sample sizes and serious overestimation of the proportion of discovered problems. This article contains descriptions and evaluations of a number of methods for adjusting small-sample estimates of the problem-discovery rate to compensate for this bias. A series of Monte Carlo simulations provided evidence that the average of a normalization procedure and Good-Turing (Jelinek, 1997; Manning & Schutze, 1999) discounting produces highly accurate estimates of usability problem-discovery rates from small sample sizes.