Seven steps toward more transparency in statistical practice

Eric-Jan Wagenmakers (), Alexandra Sarafoglou, Sil Aarts, Casper Albers, Johannes Algermissen, Štěpán Bahník, Noah Dongen, Rink Hoekstra, David Moreau, Don Ravenzwaaij, Aljaž Sluga, Franziska Stanke, Jorge Tendeiro and Balazs Aczel
Additional contact information
Eric-Jan Wagenmakers: University of Amsterdam
Alexandra Sarafoglou: University of Amsterdam
Sil Aarts: Maastricht University
Casper Albers: University of Groningen
Johannes Algermissen: Radboud University
Štěpán Bahník: Prague University of Economics
Noah Dongen: University of Amsterdam
Rink Hoekstra: University of Groningen
David Moreau: The University of Auckland
Don Ravenzwaaij: University of Groningen
Aljaž Sluga: Erasmus University Rotterdam
Franziska Stanke: University of Münster
Jorge Tendeiro: University of Groningen
Balazs Aczel: ELTE Eotvos Lorand University

Nature Human Behaviour, 2021, vol. 5, issue 11, 1473-1480

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41562-021-01211-8

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