Seven steps toward more transparency in statistical practice

Eric Jan Wagenmakers*, Alexandra Sarafoglou, Sil Aarts, Casper Albers, Johannes Algermissen, Štěpán Bahník, Noah van Dongen, Rink Hoekstra, David Moreau, Don van Ravenzwaaij, Aljaž Sluga, Franziska Stanke, Jorge Tendeiro, Balazs Aczel

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

4 Citations (Scopus)
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We argue that statistical practice in the social and behavioural sciences benefits from transparency, a fair acknowledgement of uncertainty and openness to alternative interpretations. Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data; (2) quantifying inferential uncertainty; (3) assessing data preprocessing choices; (4) reporting multiple models; (5) involving multiple analysts; (6) interpreting results modestly; and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in Merton’s ethos of science as reflected in the norms of communalism, universalism, disinterestedness and organized scepticism. We believe that these ethical considerations—as well as their statistical consequences—establish common ground among data analysts, despite continuing disagreements about the foundations of statistical inference.

Original languageEnglish
Pages (from-to)1473-1480
Number of pages8
JournalNature Human Behaviour
Issue number11
Publication statusPublished - 10 Nov 2021


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