Here, we summarise the unresolved debate about p value and its dichotomisation. We present the statement of the American Statistical Association against the misuse of statistical significance as well as the proposals to abandon the use of p value and to reduce the significance threshold from 0.05 to 0.005. We highlight reasons for a conservative approach, as clinical research needs dichotomic answers to guide decision-making, in particular in the case of diagnostic imaging and interventional radiology. With a reduced p value threshold, the cost of research could increase while spontaneous research could be reduced. Secondary evidence from systematic reviews/meta-analyses, data sharing, and cost-effective analyses are better ways to mitigate the false discovery rate and lack of reproducibility associated with the use of the 0.05 threshold. Importantly, when reporting p values, authors should always provide the actual value, not only statements of “p < 0.05” or “p ≥ 0.05”, because p values give a measure of the degree of data compatibility with the null hypothesis. Notably, radiomics and big data, fuelled by the application of artificial intelligence, involve hundreds/thousands of tested features similarly to other “omics” such as genomics, where a reduction in the significance threshold, based on well-known corrections for multiple testing, has been already adopted.

中文翻译：

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统计显着性：p 值、0.05 阈值以及在放射组学中的应用 - 保守方法的原因。


在这里，我们总结了有关p值及其二分法的尚未解决的争论。我们提出了美国统计协会反对滥用统计显着性的声明，以及放弃使用p值并将显着性阈值从 0.05 降低到 0.005 的建议。我们强调采取保守方法的原因，因为临床研究需要二分法答案来指导决策，特别是在诊断成像和介入放射学的情况下。随着p值阈值的降低，研究成本可能会增加，而自发研究可能会减少。来自系统评价/荟萃分析、数据共享和成本效益分析的次要证据是减少与使用 0.05 阈值相关的错误发现率和缺乏可重复性的更好方法。重要的是，在报告p值时，作者应始终提供实际值，而不仅仅是“ p < 0.05”或“ p ≥ 0.05”的陈述，因为p值衡量了数据与原假设的兼容性程度。值得注意的是，在人工智能应用的推动下，放射组学和大数据涉及数百/数千个测试特征，类似于基因组学等其他“组学”，其中显着性阈值的降低基于众所周知的多重测试校正，已被采纳。