<i>p</i>-Values as QWERTY: Curating Evidence in the Computational Era

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.

• 저널Journal of Marketing · 88(3) · 20–28
• 토픽Bayesian Modeling and Causal Inference · Artificial Intelligence
• DOI10.1177/00222429231221698
• 저자Fred M. Feinberg