The article challenges the use of Fixed Effects (FE) modeling as the default for time-series cross-sectional and panel data. It argues that Random Effects (RE) modeling is often more appropriate because it can account for both within and between effects, and it allows for the modeling of context and heterogeneity. RE models are more flexible and generalizable than FE models, and they can incorporate time-invariant variables, random coefficients, cross-level interactions, and complex variance functions. The article also highlights the limitations of FE models, such as their inability to estimate higher-level effects and their potential to lead to misleading interpretations due to omitted variable bias. It critiques the use of FE Vector Decomposition (FEVD) as a method for estimating time-invariant variables, arguing that it retains many of the flaws of FE models. The article proposes a solution to the problem of heterogeneity bias by explicitly modeling both within and between effects, which can be done using a within-between formulation of the RE model. This approach allows for a more accurate estimation of effects and provides a richer understanding of the relationships being studied. The article also discusses the importance of considering context and heterogeneity in multilevel datasets and argues that RE models are often the better choice for analyzing such data. The article concludes that while FE models have their place, RE models are generally more appropriate for most research questions, especially those involving hierarchical or multilevel data.The article challenges the use of Fixed Effects (FE) modeling as the default for time-series cross-sectional and panel data. It argues that Random Effects (RE) modeling is often more appropriate because it can account for both within and between effects, and it allows for the modeling of context and heterogeneity. RE models are more flexible and generalizable than FE models, and they can incorporate time-invariant variables, random coefficients, cross-level interactions, and complex variance functions. The article also highlights the limitations of FE models, such as their inability to estimate higher-level effects and their potential to lead to misleading interpretations due to omitted variable bias. It critiques the use of FE Vector Decomposition (FEVD) as a method for estimating time-invariant variables, arguing that it retains many of the flaws of FE models. The article proposes a solution to the problem of heterogeneity bias by explicitly modeling both within and between effects, which can be done using a within-between formulation of the RE model. This approach allows for a more accurate estimation of effects and provides a richer understanding of the relationships being studied. The article also discusses the importance of considering context and heterogeneity in multilevel datasets and argues that RE models are often the better choice for analyzing such data. The article concludes that while FE models have their place, RE models are generally more appropriate for most research questions, especially those involving hierarchical or multilevel data.