Morgan Josh L
Washington University in St. Louis, Department of Ophthalmology and Visual Sciences, Neuroscience, Biology and Biomedical Science.
ArXiv. 2025 May 12:arXiv:2407.07114v3.
It should not be controversial to argue that the proximate goal of measuring something is to figure out how big or small or fast or slow it is. Estimates of effect size can be used to build models of how cells work and to test quantitative predictions. Unfortunately, in cell biology, quantification is nearly synonymous with null-hypothesis significance testing. The hypothesis being tested is universally assumed to be the hypothesis that there was no effect. Framing every experiment as an attempt to reject the no-effect hypothesis is convenient but doesn't teach us about cells. In this manuscript, I walk through some of the common critiques of significance testing and how these critiques relate to experimental cell biology. I argue that careful consideration of effect size should be returned to its central position in the planning and discussion of cell biological research. To facilitate this shift in focus, I recommend replacing p-values with confidence intervals as cell biology's default statistical analysis.
认为测量某事物的直接目标是弄清楚它有多大、多小、多快或多慢，这应该是没有争议的。效应大小的估计可用于构建细胞如何工作的模型，并测试定量预测。不幸的是，在细胞生物学中，量化几乎等同于零假设显著性检验。被检验的假设普遍被认为是没有效应的假设。将每个实验都构建为试图拒绝无效应假设很方便，但并不能让我们了解细胞。在这篇手稿中，我梳理了一些对显著性检验的常见批评，以及这些批评如何与实验细胞生物学相关。我认为，在细胞生物学研究的规划和讨论中，应将对效应大小的仔细考虑恢复到其核心地位。为了促进这种重点的转变，我建议用置信区间取代p值，作为细胞生物学的默认统计分析方法。
