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The Impact of Results Blind Science Publishing on Statistical Consultation and Collaboration

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  • Joseph J. Locascio

Abstract

The author has previously proposed results blind manuscript evaluation (RBME) as a method of ameliorating often cited problems of statistical inference and scientific publication, notably publication bias, overuse/misuse of null hypothesis significance testing (NHST), and irreproducibility of reported scientific results. In RBME, manuscripts submitted to scientific journals are assessed for suitability for publication without regard to their reported results. Criteria for publication are based exclusively on the substantive importance of the research question addressed in the study, conveyed in the Introduction section of the manuscript, and the quality of the methodology, as reported in the Methods section. Practically, this policy is implemented by a two stage process whereby the editor initially distributes only the Introduction and Methods sections of a submitted manuscript to reviewers and a provisional decision regarding acceptance is made, followed by a second stage in which the complete manuscript is distributed for review but only if the decision of the first stage is for acceptance. The present paper expands upon this recommendation by addressing implications of this proposed policy with respect to statistical consultation and collaboration in research. It is suggested that under RBME, statisticians will become more integrated into research endeavors and called upon sooner for their input.

Suggested Citation

  • Joseph J. Locascio, 2019. "The Impact of Results Blind Science Publishing on Statistical Consultation and Collaboration," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 346-351, March.
  • Handle: RePEc:taf:amstat:v:73:y:2019:i:s1:p:346-351
    DOI: 10.1080/00031305.2018.1505658
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    Cited by:

    1. Zenker, Frank & Witte, Erich H., 2021. "The case for default point-H1-hypotheses: a theory-construction perspective," OSF Preprints zue4h, Center for Open Science.

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