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A meta-analyst can choose between a ‘fixed effect’ model and a ‘random effects’ model. In the ‘fixed effect’ model it is assumed that all differences between effect sizes observed in different studies are only due to sampling error. In other words, it is assumed that there is no “heterogeneity”. In the ‘random effects’ model it is assumed that there is heterogeneity. The assumptions underlying the fixed effect model are very rarely met. Furthermore, when a fixed effect model would make sense to use, i.e., when there is little variance in effect sizes, the random effects model automatically converges into a fixed effect model. Therefore, it is strongly recommended to always use the random effects model, and to interpret the heterogeneity measures before deciding to use the fixed effect model (if at all).