Random effects are a statistical concept used in models to account for variability across different groups or subjects. In these models, some factors are treated as random variables, meaning their effects can vary randomly across the population. This approach is particularly useful in situations where data is collected from multiple sources, such as in longitudinal studies or multilevel modeling. By incorporating random effects, researchers can better understand the influence of both fixed factors, which are consistent across the population, and random factors, which introduce variability. This helps improve the accuracy of predictions and provides insights into the underlying structure of the data, making it a valuable tool in fields like economics and psychology.