Estimating Completion Rates from Small Samples Using Binomial Confidence Intervals: Comparisons and Recommendations

The completion rate — the proportion of participants who successfully complete a task — is a common usability measurement. As is true for any point measurement, practitioners should compute appropriate confidence intervals for completion rate data. For proportions such as the completion rate, the appropriate interval is a binomial confidence interval. The most widely-taught method for calculating binomial confidence intervals (the “Wald Method,” discussed both in introductory statistics texts and in the human factors literature) grossly understates the width of the true interval when sample sizes are small. Alternative “exact” methods over-correct the problem by providing intervals that are too conservative. This can result in practitioners unintentionally accepting interfaces that are unusable or rejecting interfaces that are usable. We examined alternative methods for building confidence intervals from small sample completion rates, using Monte Carlo methods to sample data from a number of real, large-sample usability tests. It appears that the best method for practitioners to compute 95% confidence intervals for small-sample completion rates is to add two successes and two failures to the observed completion rate, then compute the confidence interval using the Wald method (the “Adjusted Wald Method”). This simple approach provides the best coverage, is fairly easy to compute, and agrees with other analyses in the statistics literature.