Linear Mixed Models (LMMs) are statistical tools used to analyze data that have both fixed and random effects. Fixed effects are consistent across all observations, while random effects vary. This makes LMMs particularly useful for data collected from hierarchical or grouped structures, such as measurements taken from multiple subjects over time. LMMs allow researchers to account for the variability within and between groups, providing more accurate estimates of relationships in the data. They are commonly applied in fields like psychology, biology, and education to analyze repeated measures or clustered data, enhancing the understanding of complex phenomena.