Mixed Effects Models are statistical tools used to analyze data that have multiple sources of variability. They combine fixed effects, which are consistent across all observations, with random effects, which account for variations specific to individual subjects or groups. This approach is particularly useful in fields like psychology, biology, and economics, where data often come from different sources or conditions. These models help researchers understand complex relationships in their data by allowing for both general trends and individual differences. By incorporating both fixed and random effects, mixed effects models provide a more accurate representation of the underlying processes, leading to better predictions and insights.