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

TL;DR

The paper introduces ExGUtils, a Python package designed for efficient statistical analysis of reaction time data modeled by the ex-Gaussian distribution, facilitating better data fitting, outlier detection, and methodological comparison.

Contribution

It provides a versatile computational tool for analyzing ex-Gaussian data, including fit validation and outlier detection, which was lacking in existing resources.

Findings

ExGUtils effectively fits ex-Gaussian data with high accuracy.

Comparison of least squares and maximum likelihood methods highlights their advantages.

The package aids in identifying outliers and determining data trimming needs.

Abstract

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 fit to multiple empirical data. The complexity of this distribution makes the use of computational tools an essential element in the field. 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 fits obtained with it, discuss advantages and differences between the least squares and maximum likelihood methods and quantitatively evaluate the goodness of the obtained fits (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.