Fixed- vs. Random-Effects Models: 5 Tips to Get A Better Understanding .

Fixed- and random-effects models shape how meta-analyses pool study results, and they differ mainly in their assumptions about variation across studies. A fixed-effects model treats studies as estimating one shared effect, while a random-effects model assumes meaningful differences and averages a distribution of effects. These approaches assign weights differently, with fixed-effects leaning heavily on larger studies and random-effects giving smaller studies more influence. When heterogeneity is low, results from both models may look similar, but when variation is substantial, estimates can diverge. Random-effects is often preferred for its broader applicability, though fixed-effects may be suitable when a single large, credible study dominates the evidence.