The statistical analysis of answer changes (ACs) has uncovered multiple testing irregularities on large-scale assessments and is now routinely performed at testing organizations. However, AC data has an uncertainty caused by technological or human factors. Therefore, existing statistics (e.g., number of wrong-to-right ACs) used to detect examinees with aberrant ACs capitalize on the uncertainty, which may result in a large Type I error. In this article, the information about ACs is used only for the partitioning of administered items into two disjoint subtests: items where ACs did not occur, and items where ACs did occur. A new statistic is based on the difference in performance between these subtests (measured as Kullback-Leibler divergence between corresponding posteriors of latent traits), where, in order to avoid the uncertainty, only final responses are used. One of the subtests can be filtered such that the asymptotic distribution of the statistic is chi-square with one degree of freedom. In computer simulations, the presented statistic demonstrated a strong robustness to the uncertainty and higher detection rates in contrast to two popular statistics based on wrong-to-right ACs.