Mixed effects models are statistical models that incorporate both fixed and random effects, allowing for the analysis of data with multiple levels of variability. In the context of catalysis, they enable researchers to account for both the consistent effects of certain variables (fixed effects) and the random variability that may occur due to other factors (random effects).