Mixed-effects models 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 among different groups or subjects. This approach is particularly useful in situations where data is collected from multiple sources or over time, allowing researchers to account for variability within and between groups. These models are commonly applied in fields like psychology, ecology, and economics, where researchers need to understand complex relationships in their data. By incorporating both types of effects, mixed-effects models provide a more accurate representation of the underlying processes influencing the observed outcomes.