A Mixed Effects Model is a statistical technique 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. These models are commonly applied in fields such as psychology, biology, and economics, where researchers often deal with hierarchical or clustered data. By incorporating both types of effects, mixed models provide a more accurate understanding of complex relationships in the data.