A Linear Mixed Model (LMM) is a statistical method used to analyze data that involves both fixed and random effects. Fixed effects are consistent across all observations, while random effects vary. This model is particularly useful in situations where data is collected from multiple groups or subjects, allowing researchers to account for individual differences and correlations within the data. LMMs are commonly applied in fields such as psychology, biology, and economics to analyze repeated measures or hierarchical data structures. By incorporating both types of effects, LMMs provide a more accurate representation of complex data, improving the reliability of conclusions drawn from the analysis.