Subgroup analyses are often reported in randomized controlled trials and meta-analyses. Apparent subgroup effects may, however, be misleading. Surgeons may therefore find it challenging to decide whether to believe a claim of subgroup effect (i.e., an apparent difference in treatment effect between subgroups of the study population). In the present study, we introduce seven widely used criteria to assess subgroup analyses in the surgical literature and include two examples of subgroup analyses from a large randomized trial to elaborate on the use of these criteria. Typically, inferences regarding subgroup effects are stronger if the comparison is made within rather than between studies, if the test for interaction suggests that chance is an unlikely explanation for apparent differences, if the subgroup hypothesis was specified a priori, if it was one of a small number of hypotheses tested, if the difference in effect between subgroup categories is large, if it is consistent across studies, and if there is indirect evidence supporting the difference (a biological rationale).