This article challenges the dominance of Fixed Effects (FE) modeling in time-series cross-sectional and panel data analysis, advocating for Random Effects (RE) modeling. The authors argue that RE models, which can account for correlated lower-level covariates and higher-level residuals, offer a more robust solution to omitted-variable bias, as demonstrated by Mundlak's (1978a) formulation. They show that RE models can provide all the benefits of FE models and more, as confirmed by Monte-Carlo simulations, which also highlight issues with Plümper and Troeger's FE Vector Decomposition method in unbalanced data. The article emphasizes the importance of modeling context and heterogeneity using RE models, which can incorporate time-invariant variables, random coefficients, cross-level interactions, and complex variance functions. While RE models are not without their limitations, the authors argue that they offer a more comprehensive and flexible approach to hierarchical data analysis, particularly in social sciences.This article challenges the dominance of Fixed Effects (FE) modeling in time-series cross-sectional and panel data analysis, advocating for Random Effects (RE) modeling. The authors argue that RE models, which can account for correlated lower-level covariates and higher-level residuals, offer a more robust solution to omitted-variable bias, as demonstrated by Mundlak's (1978a) formulation. They show that RE models can provide all the benefits of FE models and more, as confirmed by Monte-Carlo simulations, which also highlight issues with Plümper and Troeger's FE Vector Decomposition method in unbalanced data. The article emphasizes the importance of modeling context and heterogeneity using RE models, which can incorporate time-invariant variables, random coefficients, cross-level interactions, and complex variance functions. While RE models are not without their limitations, the authors argue that they offer a more comprehensive and flexible approach to hierarchical data analysis, particularly in social sciences.