Mixed 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 certain groups or conditions. This approach is particularly useful in fields like psychology, biology, and economics, where data may come from different subjects or experimental conditions. By incorporating both fixed and random effects, mixed models can provide more accurate estimates and predictions. They help researchers understand complex relationships in data, such as how individual differences influence overall trends, making them valuable for analyzing repeated measures or hierarchical data structures.