In statistics classes, students often learn a zoo of different models, without grasping how those all link together. In consequence, they often fit several models, one for each research question. Hierarchical/ multi-level/ mixed-effects models constitute a unifying framework that allows to address many questions in a single model, including questions that are not easily answered with standard ANOVAs (e.g. trial-by-trial effects). However, this increased flexibility comes at the costs of more complex computation. We will discuss the logic and benefits/ pitfalls of using mixed-effects models, and then introduce Markov chain Monte Carlo (MCMC) as a particularly suited fitting algorithm. We will introduce the R package brms, an easy-to-handle wrapper for fitting Bayesian mixed-effects models using MCMCs in the language Stan.