{"paper":{"title":"Beyond Publication-Bias Detection: Estimating Bias under Uncertainty in Heterogeneous Literatures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Ulrich Schimmack","submitted_at":"2026-07-21T23:16:52Z","abstract_excerpt":"Meta-analysts routinely test for publication bias, but a nonsignificant test is inconclusive because it may be a Type 2 error. In a large factorial simulation, I show that publication-bias tests have low power even with 1,000 studies once effect sizes are heterogeneous and the data do not meet the model's assumptions. I argue that the goal should shift from detecting bias to estimating it with confidence intervals. A confidence interval does not only bound the hypothesis that bias is absent; its upper bound also indicates how much bias remains compatible with the data. When the interval is wid"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.19626","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.19626/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}