Center for Data Analytics, Innovation, and Rigor, Child Mind Institute, New York, NY, USA.
Department of Psychology, Stanford University, Stanford, CA, USA.
Nat Commun. 2024 Oct 31;15(1):9411. doi: 10.1038/s41467-024-53743-y.
In a perfect world, scientists would develop analyses that are guaranteed to reveal the ground truth of a research question. In reality, there are countless viable workflows that produce distinct, often conflicting, results. Although reproducibility places a necessary bound on the validity of results, it is not sufficient for claiming underlying validity, eventual utility, or generalizability. In this work we focus on how embracing variability in data analysis can improve the generalizability of results. We contextualize how design decisions in brain imaging can be made to capture variation, highlight examples, and discuss how variability capture may improve the quality of results.
在理想世界中，科学家会开发出能够保证揭示研究问题真相的分析方法。但实际上，有无数可行的工作流程会产生截然不同、往往相互矛盾的结果。尽管可重复性对结果的有效性施加了必要的限制，但它不足以声称其具有潜在的有效性、最终的实用性或可推广性。在这项工作中，我们专注于如何接受数据分析中的可变性，以提高结果的可推广性。我们从背景出发，探讨了脑成像设计决策如何被用来捕捉变异性，突出了一些示例，并讨论了变异性捕捉如何提高结果的质量。
