Usability testing is recognized as an effective means to improve the usability of medical devices and prevent harm for patients and users. Effectiveness of problem discovery in usability testing strongly depends on size and representativeness of the sample. We introduce the late control strategy, which is to continuously monitor effectiveness of a study towards a preset target. A statistical model, the LNBzt model, is presented, supporting the late control strategy. We report on a case study, where a prototype medical infusion pump underwent a usability test with 34 users. On the data obtained in this study, the LNBzt model is evaluated and compared against earlier prediction models. The LNBzt model fits the data much better than previously suggested approaches and improves prediction. We measure the effectiveness of problem identification, and observe that it is lower than is suggested by much of the literature. Larger sample sizes seem to be in order. In addition, the testing process showed high levels of uncertainty and volatility at small to moderate sample sizes, partly due to users’ individual differences. In reaction, we propose the idiosyncrasy score as a means to obtain representative samples. Statistical programs are provided to assist practitioners and researchers in applying the late control strategy.