Publication bias

The bias arises at several points: authors abandon null studies, reviewers and editors favor positive ones, and journals prize novelty. The result is a body of evidence that overrepresents effects that are real but exaggerated, or not real at all. When many teams study the same question, the published subset is systematically unrepresentative of what was actually found.

Meta-analysts try to detect it with funnel plots, which should be symmetric if small and large studies agree, and with tests such as Egger's regression. Asymmetry suggests missing small null studies. These diagnostics are imperfect, since asymmetry can have other causes, but they remain standard practice in evidence synthesis.

Structural fixes target the source. Trial registries, mandatory results reporting, registered reports, and journals dedicated to null findings all aim to bring the file drawer into the open. For reviewers of a systematic review, whether the authors searched for unpublished data and assessed publication bias is a key quality check.