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Why is the statistical revolution not progressing? Vicious cycle of the scientific reform

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  • Bialek, Michal

    (University of Wroclaw)

  • Misiak, Michał

    (University of Wroclaw)

  • Dziekan, Martyna

Abstract

To analyze the results of the research, behavioral scientists widely use a statistical rule that sets the significance level to 0.05. Recently, two recommendations on how to improve statistical inference were published: to redefine statistical significance to 0.005, and to select and justify the alpha. We analyzed the empirical work that cited the original recommendation papers, as well as the papers published by the scientist that co-authored the publications. About half of the numerous papers citing these recommendations adhered to them already in the first year since their publication. What is striking, the original authors that proposed the recommendations followed their own recommendations only in 6% of their empirical work. We surveyed the authors asking them to identify major obstacles they experienced while trying to implement their own recommendations, and obstacles they think others could expect or experience.

Suggested Citation

  • Bialek, Michal & Misiak, Michał & Dziekan, Martyna, 2021. "Why is the statistical revolution not progressing? Vicious cycle of the scientific reform," OSF Preprints gmfs9_v1, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:gmfs9_v1
    DOI: 10.31219/osf.io/gmfs9_v1
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    References listed on IDEAS

    as
    1. Georgios Gerasimou, 2016. "Asymmetric dominance, deferral, and status quo bias in a behavioral model of choice," Theory and Decision, Springer, vol. 80(2), pages 295-312, February.
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      • Daniel Benjamin & James Berger & Magnus Johannesson & Brian Nosek & E. Wagenmakers & Richard Berk & Kenneth Bollen & Bjorn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Christopher Chambe, 2017. "Redefine Statistical Significance," Artefactual Field Experiments 00612, The Field Experiments Website.
    3. Abel Brodeur & Nikolai Cook & Anthony Heyes, 2020. "Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics," American Economic Review, American Economic Association, vol. 110(11), pages 3634-3660, November.
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