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dc.contributor.authorMcShane, Blakeley B.*
dc.contributor.authorGal, David*
dc.contributor.authorGelman, Andrew*
dc.contributor.authorRobert, Christian P.*
dc.contributor.authorTackett, Jennifer L.*
dc.date.accessioned2019-04-16T08:56:26Z
dc.date.available2019-04-16T08:56:26Z
dc.date.issued2019
dc.identifier.issn0003-1305
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18654
dc.language.isoenen
dc.subjectnull hypothesis significance testing
dc.subjectstatistical significance
dc.subjectp-value
dc.subjectsociology ofscience
dc.subjectreplication
dc.subject.ddc515en
dc.titleAbandon Statistical Significance
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these problems remain unresolved by proposals involving modified p-value thresholds, confidence intervals, and Bayes factors. We then discuss our own proposal, which is to abandon statistical significance. We recommend dropping the NHST paradigm--and the p-value thresholds intrinsic to it--as the default statistical paradigm for research, publication, and discovery in the biomedical and social sciences. Specifically, we propose that the p-value be demoted from its threshold screening role and instead, treated continuously, be considered along with currently subordinate factors (e.g., related prior evidence, plausibility of mechanism, study design and data quality, real world costs and benefits, novelty of finding, and other factors that vary by research domain) as just one among many pieces of evidence. We have no desire to ban" p-values or other purely statistical measures. Rather, we believe that such measures should not be thresholded and that, thresholded or not, they should not take priority over the currently subordinate factors. We also argue that it seldom makes sense to calibrate evidence as a function of p-values or other purely statistical measures. We offer recommendations for how our proposal can be implemented in the scientific publication process as well as in statistical decision making more broadly."
dc.publisher.cityParisen
dc.relation.isversionofjnlnameThe American Statistician
dc.relation.isversionofjnlvol73
dc.relation.isversionofjnlissueSup.1
dc.relation.isversionofjnldate2019
dc.relation.isversionofjnlpages235-245
dc.relation.isversionofdoi10.1080/00031305.2018.1527253
dc.relation.isversionofjnlpublisherAmerican Statistical Association
dc.subject.ddclabelAnalyseen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2020-04-28T12:15:06Z
hal.person.labIds200922*
hal.person.labIds*
hal.person.labIds85041*
hal.person.labIds*
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