Generalized Linear Mixed Models (GLMMs) are statistical tools used to analyze data that involve both fixed effects and random effects. Fixed effects are consistent across all observations, while random effects account for variability among different groups or clusters within the data. This makes GLMMs particularly useful for handling complex data structures, such as those found in longitudinal studies or hierarchical data. GLMMs extend traditional linear models by allowing for non-normal response variables, such as binary or count data. They combine the principles of generalized linear models with mixed effects, enabling researchers to make inferences about population-level effects while also considering individual-level variations. This flexibility makes GLMMs valuable in various fields, including ecology, psychology, and medicine.