Random effects are a statistical method used in research to account for and analyze variability within and between groups or subjects in a study. This approach acknowledges that there may be inherent differences between groups or individuals that could impact the outcome of the study. By incorporating random effects into the analysis, researchers can better estimate the overall variance in the data and make more accurate inferences about the relationships between variables. Random effects are often used in mixed-effects models, hierarchical models, and multilevel models to account for both fixed effects (predictors of interest) and random effects (sources of variability). This method is particularly useful in longitudinal studies, repeated measures designs, and other complex research settings where there may be multiple levels of variability to consider.