Seven steps toward more transparency in statistical practice

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. Wagenmakers and colleagues describe seven statistical procedures that increase transparency in data analysis. These procedures highlight common ground among data analysts from different schools and find inspiration in Merton's ethos of science.

Automated summary

This article presents seven statistical procedures that increase transparency in data analysis, aiming to promote transparent practices in social and behavioral sciences. These procedures highlight common ground among data analysts from different schools and find inspiration in the ethos of science.