Random Effects Models are statistical tools used in data analysis to account for variability across different groups or subjects. They assume that the differences between these groups are random and not fixed, allowing researchers to make inferences about a larger population. This approach is particularly useful in situations where data is collected from multiple sources, such as in longitudinal studies or multilevel modeling. In a Random Effects Model, the effects of certain variables are treated as random variables, which helps to capture the inherent variability in the data. This model contrasts with Fixed Effects Models, where the effects are considered constant across groups. By incorporating random effects, researchers can better understand the influence of both individual and group-level factors on the outcome of interest.