Linear mixed models (LMMs) are a powerful statistical tool for analyzing hierarchical or grouped data, common in neuroscience experiments. This post provides a practical guide on when to use LMMs versus traditional ANCOVA approaches, highlighting the advantages of mixed models in handling dependencies, unbalanced designs, and stabilizing estimates through shrinkage. Through simulated examples, we illustrate the differences in model performance and interpretation, helping you to make informed decisions about your statistical analyses.