Scientific self-correction: the Bayesian way.

The enduring replication crisis in many scientific disciplines casts doubt on the ability of science to estimate effect sizes accurately, and in a wider sense, to self-correct its findings and to produce reliable knowledge. We investigate the merits of a particular countermeasure—replacing null hypo...

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Publicado en:Synthese Vol. 198; no. 23; pp. 5803 - 5824
Autores principales: Romero, Felipe, Sprenger, Jan
Formato: Artículo
Publicado: Springer Nature Oct2021 Supplement 23
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Scientific self-correction: the Bayesian way.
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        au:
          Romero, Felipe
          Sprenger, Jan
        affil:
          Faculty of Philosophy, University of Groningen, Oude Boteringestraat 52, 9712 GL, Groningen, The Netherlands
          Center for Logic, Language and Cognition (LLC) Department of Philosophy and Education Sciences, Università degli Studi di Torino, Palazzo Nuovo, Via Sant'Ottavio 20, 10124, Torino, Italy
      su:
        Scientific ability
        Null hypothesis
        Bayesian field theory
        Behavioral sciences
        Behavioral research
      sug:
        subj:
          Scientific ability
          Null hypothesis
          Bayesian field theory
          Behavioral sciences
          Behavioral research
      keyword:
        Bayesian statistics
        Null hypothesis significance testing (NHST)
        Replication crisis
        Self-corrective thesis
        Statistical inference
        Statistical reform
      ab: The enduring replication crisis in many scientific disciplines casts doubt on the ability of science to estimate effect sizes accurately, and in a wider sense, to self-correct its findings and to produce reliable knowledge. We investigate the merits of a particular countermeasure—replacing null hypothesis significance testing (NHST) with Bayesian inference—in the context of the meta-analytic aggregation of effect sizes. In particular, we elaborate on the advantages of this Bayesian reform proposal under conditions of publication bias and other methodological imperfections that are typical of experimental research in the behavioral sciences. Moving to Bayesian statistics would not solve the replication crisis single-handedly. However, the move would eliminate important sources of effect size overestimation for the conditions we study.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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