Mixed models, also known as multilevel models or hierarchical models, are statistical models that incorporate both fixed effects and random effects. Fixed effects refer to factors that are of interest in the study and are assumed to be constant across all observations, while random effects account for variation that is specific to individual subjects or groups. Mixed models are commonly used in fields such as social sciences, epidemiology, and biology to account for hierarchical data structures, repeated measures, and clustering of observations. These models allow for the estimation of both within-group and between-group variability, making them useful for analyzing complex data sets with nested or longitudinal structures.