Empirical analyses in social science frequently confront quantitative data that are clustered or grouped. To account for group-level variation and improve model fit, researchers will commonly specify either a fixed or random effects model. But current advice on which approach should be preferred, and under what conditions, remains vague and sometimes contradictory. We perform a series of Monte Carlo simulations to evaluate the total error due to bias and variance in the inferences of each model, for typical sizes and types of datasets encountered in applied research. Our results offer a typology of dataset characteristics to aid researchers in choosing a preferred model.