This tutorial for the statistical processing of reaction times collected through a repeated-measure design is addressed to researchers in psychology. It aims at making explicit some important methodological issues, at orienting researchers to the existing solutions, and at providing them some evaluation tools for choosing the most robust and precise way to analyze their data. The methodological issues we tackle concern data filtering, missing values management, and statistical modeling (F1, F2, F1 + F2, quasi-F, mixed-effects models with hierarchical, or with crossed factors). For each issue, references and remedy suggestions are given. In addition, modeling techniques are compared on real data and a benchmark is given for estimating the precision and robustness of each technique. The experimental method provides a highly efficient means for confronting theoretical models with reality in order to build and improve our understanding of nature. In an attempt to increase the scientific nature of their young discipline, psychologists adopted the experimental method as away to systematically describe and study human behavior. For this enterprise, they needed to “measure variables objectively,” and “accurately infer that the observed pattern of results reflects what typically happens” (Mitchell & Jolley, 2007). In other words, they needed to use statistical quantitative techniques. Statistical quantitative techniques are complex mathematical methods mainly used to draw linear relations between variables. These methods must be handled carefully in order to avoid biased conclusions and the creation of fallacious © Cambridge