Bias can enter before, during, or after a study is conducted. Researchers may choose not to initiate a question, withhold a submission, or selectively report results; sponsors, editors, and reviewers can also influence which findings become visible. Because these decisions operate at several points, correcting the published record requires more than examining editorial acceptance alone.
Null or unfavorable findings can disappear from the evidence base, leaving the available studies disproportionately favorable. In a systematic review or meta-analysis, that imbalance can inflate the estimated effect and make confidence in the conclusion seem stronger than warranted. The resulting distortion can then affect evidence-based decisions, even when the underlying research record contains contrary results.
The problem is not confined to what appears in an article. It may reflect selective initiation, submission, acceptance, or reporting, so a visible result can be shaped before analysis is published. This broader pathway matters statistically because the missing evidence is difficult to observe directly, limiting how confidently researchers can interpret the published record.
Statistical assessment commonly combines funnel plots, regression-based tests, and sensitivity analyses. These approaches give reviewers several ways to examine whether the available evidence shows signs of selective visibility and whether conclusions change under alternative assumptions. They are especially relevant when a review or meta-analysis produces an apparently strong or favorable estimate.
Preregistration and complete results reporting address publication bias by making it harder for studies or findings to remain invisible after research begins. These practices are preventive rather than purely diagnostic: they aim to preserve a fuller evidence base before a systematic review or meta-analysis estimates an overall effect.
Readers should consider publication bias when applying statistical evidence to decisions, not only when evaluating individual study quality. If the visible literature may overestimate effects, conclusions should be interpreted cautiously and checked with bias assessments and sensitivity analyses. This is important wherever systematic reviews or meta-analyses guide evidence-based decisions.