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.

我们认为，社会和行为科学中的统计实践受益于透明度、对不确定性和对替代解释的开放性的公平承认。在这里，为了促进这种做法，我们推荐七个具体的统计程序：（1）可视化数据；(2) 量化推断的不确定性；(3) 评估数据预处理选择；(4) 报告多个模型；（五）涉及多名分析人员；(6) 虚心解读结果；(7) 共享数据和代码。我们讨论了它们的好处和局限性，并提供了采用指南。七个程序中的每一个都在默顿的科学精神中找到了灵感，这反映在社区主义、普遍主义、无私和有组织的怀疑主义的规范中。