McShane et al.'s (2024) wide-ranging critique of null hypothesis significance testing provides a number of specific suggestions for improved practice in empirical research. This commentary amplifies several of these from the perspective of computational statistics—particularly nonparametrics, resampling/bootstrapping, and Bayesian methods—applied to common research problems. Throughout, the author emphasizes estimation (as opposed to testing) and uncertainty quantification through a comprehensive process of “curating” a variety of graphical and tabular evidence. Specifically, researchers should be encouraged to estimate the quantities that matter, with as few assumptions as possible, in multiple ways, then try to visualize it all, documenting their pathway from data to results for others to follow.

中文翻译：

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p 值作为 QWERTY：在计算时代整理证据

McShane 等人 (2024) 对零假设显着性检验的广泛批评为改进实证研究实践提供了许多具体建议。本评论从计算统计学的角度，特别是非参数、重采样/自举和贝叶斯方法，对常见研究问题的应用，放大了其中的一些内容。自始至终，作者都强调通过“整理”各种图形和表格证据的综合过程来进行估计（而不是测试）和不确定性量化。具体来说，应该鼓励研究人员以尽可能少的假设，以多种方式估计重要的数量，然后尝试将其全部可视化，记录他们从数据到结果的路径，以供其他人遵循。