We argue that statistical practice in the social and behavioral sciences benefits from transparency, a fair acknowledgement of uncertainty, and openness to alternative interpretations. 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 skepticism. We believe that these ethical considerations --and their statistical consequences-- establish common ground among data analysts, despite continuing disagreements about the foundations of statistical inference.