This article deals with inconsistency of relative (rather than absolute) treatment effects in binary/dichotomous outcomes. A body of evidence is not rated up in quality if studies yield consistent results, but may be rated down in quality if inconsistent. Criteria for evaluating consistency include similarity of point estimates, extent of overlap of confidence intervals, and statistical criteria including tests of heterogeneity and I(2). To explore heterogeneity, systematic review authors should generate and test a small number of a priori hypotheses related to patients, interventions, outcomes, and methodology. When inconsistency is large and unexplained, rating down quality for inconsistency is appropriate, particularly if some studies suggest substantial benefit, and others no effect or harm (rather than only large vs. small effects). Apparent subgroup effects may be spurious. Credibility is increased if subgroup effects are based on a small number of a priori hypotheses with a specified direction; subgroup comparisons come from within rather than between studies; tests of interaction generate low P-values; and have a biological rationale.