  Frequentist statistical methods, such as hypothesis testing, are standard
practice in papers that provide benchmark comparisons. Unfortunately, these
methods have often been misused, e.g., without testing for their statistical
test assumptions or without controlling for family-wise errors in multiple
group comparisons, among several other problems. Bayesian Data Analysis (BDA)
addresses many of the previously mentioned shortcomings but its use is not
widely spread in the analysis of empirical data in the evolutionary computing
community. This paper provides three main contributions. First, we motivate the
need for utilizing Bayesian data analysis and provide an overview of this
topic. Second, we discuss the practical aspects of BDA to ensure that our
models are valid and the results transparent. Finally, we provide five
statistical models that can be used to answer multiple research questions. The
online appendix provides a step-by-step guide on how to perform the analysis of
the models discussed in this paper, including the code for the statistical
models, the data transformations and the discussed tables and figures.
