Misalignment Between Research Hypotheses and Statistical Hypotheses: A Threat to Evidence-Based Medicine?

Evidence-based medicine frequently uses statistical hypothesis testing. In this paradigm, data can only disconfirm a research hypothesis' competitors: One tests the negation of a statistical hypothesis that is supposed to correspond to the research hypothesis. In practice, these hypotheses are often...

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Publicado en:Topoi: An International Review of Philosophy Vol. 40; no. 2; pp. 307 - 319
Autores principales: Lawler, Insa, Zimmermann, Georg
Formato: Artículo
Publicado: Springer Nature Apr2021
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Apr2021
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      pub: Springer Nature
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        10.1007/s11245-019-09667-0
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          Lawler, Insa
          Zimmermann, Georg
        affil:
          Department of Philosophy, University of North Carolina at Greensboro, PO Box 26170, 234 Curry Building, 27412-5001, Greensboro, NC, USA
          Department of Neurology, Christian Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria
          Department of Mathematics, Paris Lodron University of Salzburg, Salzburg, Austria
          Spinal Cord Injury and Tissue Regeneration Centre Salzburg, Paracelsus Medical University, Salzburg, Austria
      su:
        Evidence-based medicine
        Statistical hypothesis testing
        Hypothesis
        Statistical power analysis
        Decision making
        Negation (Logic)
      sug:
        subj:
          Evidence-based medicine
          Statistical hypothesis testing
          Hypothesis
          Statistical power analysis
          Decision making
          Negation (Logic)
      keyword:
        Clinical decision making
        Null hypotheses
        Research hypotheses
      ab: Evidence-based medicine frequently uses statistical hypothesis testing. In this paradigm, data can only disconfirm a research hypothesis' competitors: One tests the negation of a statistical hypothesis that is supposed to correspond to the research hypothesis. In practice, these hypotheses are often misaligned. For instance, directional research hypotheses are often paired with non-directional statistical hypotheses. Prima facie, one cannot gain proper evidence for one's research hypothesis employing a misaligned statistical hypothesis. This paper sheds lights on the nature of and the reasons for such misalignments and it provides a thorough analysis of whether they pose a threat to evidence-based medicine. The upshots are that the misalignments are often hidden for clinicians and that although some cases of misalignments can be partially counterbalanced, the overall threat is non-negligible. The counterbalances either lead to methodological inadequacy (in addition to the misalignment), loss of statistical power, or involve a (potential) lack of information that could be crucial for decision making. This result casts doubt on various findings of medical studies in addition to issues associated with under-powered studies or the replication crisis.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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