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Unlike\n                    <jats:italic>p<\/jats:italic>\n                    -values, Bayes factors allow for quantifying support both for and against the existence of an effect, facilitate ongoing evidence monitoring, and maintain coherent long-run behavior as additional studies are incorporated. Recent theoretical developments further show how Bayes factors can flexibly control Type I error rates through connections to e-value theory. Despite these advantages, their use remains limited in the meta-analytic literature. This article provides a critical overview of their theoretical properties, methodological considerations\u2014such as prior sensitivity\u2014and practical advantages for evidence synthesis. Two illustrative applications are provided: one on statistical learning in individuals with language impairments, and another on seroma incidence following post-operative exercise in breast cancer patients. New tools supporting these methods are available in the open-source R package BFpack.\n                  <\/jats:p>","DOI":"10.1017\/rsm.2025.10060","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T05:03:52Z","timestamp":1764824632000},"page":"589-623","source":"Crossref","is-referenced-by-count":6,"title":["Bayes factor hypothesis testing in meta-analyses: Practical advantages and methodological considerations"],"prefix":"10.1017","volume":"17","author":[{"given":"Joris","family":"Mulder","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04b8v1s79","id-type":"ROR","asserted-by":"publisher"}],"name":"Tilburg University, Tilburg School of Social and Behavioral Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6187-0665","authenticated-orcid":false,"given":"Robbie C. 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