The study of reaction times and their underlying cognitive processes is an important □field in Psychology. Reaction times are usually modeled through the ex-Gaussian distribution, because it provides a good □t to multiple empirical data. The complexity of this distribution makes the use of computational tools an essential element in the □eld. Therefore, there is a strong need for efficient and versatile computational tools for the research in this area. In this manuscript we discuss some mathematical details of the ex-Gaussian distribution and apply the ExGUtils package, a set of functions and numerical tools, programmed for python, developed for numerical analysis of data involving the ex-Gaussian probability density. In order to validate the package, we present an extensive analysis of fi□ts obtained with it, discuss advantages and differences between the least squares and maximum likelihood methods and quantitatively evaluate the goodness of the obtained fi□ts (which is usually an overlooked point in most literature in the area). The analysis done allows one to identify outliers in the empirical datasets and criteriously determine if there is a need for data trimming and at which points it should be done. keywords: ex-Gaussian distribution, significance testing, reaction times