Mixed models are statistical tools used to analyze data that involve both fixed and random effects. Fixed effects are consistent across all observations, while random effects vary. This approach is particularly useful in situations where data is collected from different groups or over time, allowing researchers to account for variability within and between these groups. These models are commonly applied in fields like psychology, biology, and economics, where researchers may want to understand the impact of certain variables while considering the influence of random factors. By using mixed models, analysts can obtain more accurate estimates and make better predictions based on their data.