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The study of reaction times and their underlying cognitive processes is animportant field in Psychology. Reaction times are usually modeled through theex-Gaussian distribution, because it provides a good fit to multiple empiricaldata. The complexity of this distribution makes the use of computational toolsan essential element in the field. Therefore, there is a strong need forefficient and versatile computational tools for the research in this area. Inthis manuscript we discuss some mathematical details of the ex-Gaussiandistribution and apply the ExGUtils package, a set of functions and numericaltools, programmed for python, developed for numerical analysis of datainvolving the ex-Gaussian probability density. In order to validate thepackage, we present an extensive analysis of fits obtained with it, discussadvantages and differences between the least squares and maximum likelihoodmethods and quantitatively evaluate the goodness of the obtained fits (which isusually an overlooked point in most literature in the area). The analysis doneallows one to identify outliers in the empirical datasets and criteriouslydetermine if there is a need for data trimming and at which points it should bedone.

ExGUtils: A python package for statistical analysis with the ex-gaussian probability density