当证据与意义发生冲突时
When Evidence and Significance Collide
摘要：
原假设统计显著性检验（NHST）是评价随机对照试验结果的主要方法。尽管NHST具有长期错误率保证，但它的主要推理工具--$p$值--只是针对原假设的间接证据度量。主要原因是$p$值基于原假设为真的假设，而忽略了任何备择假设下数据的可能性。如果目标是量化数据提供多少支持或反对原假设的证据，则不可避免地要指定一个备择假设（Goodman & Royall，1988）。当研究人员将$p$ -值解释为证据时，出现了Parasites。例如，在空值下令人惊讶的结果在合理的备择假设下可能同样令人惊讶，因此$p=.045$结果（“拒绝空值”）不会使空值比以前更不合理。因此，$p$值被认为高估了反对原假设的证据。相反，可能的情况是统计上不显著的结果（即，然而，$p>.05)$提供了一些支持备择假设的证据。因此，对于研究人员来说，了解统计意义和证据何时发生冲突至关重要，这需要计算证据的直接测量并与传统的$p$值一起呈现。
英文摘要：
Null hypothesis statistical significance testing (NHST) is the dominant approach for evaluating results from randomized controlled trials. Whereas NHST comes with long-run error rate guarantees, its main inferential tool -- the $p$-value -- is only an indirect measure of evidence against the null hypothesis. The main reason is that the $p$-value is based on the assumption the null hypothesis is true, whereas the likelihood of the data under any alternative hypothesis is ignored. If the goal is to quantify how much evidence the data provide for or against the null hypothesis it is unavoidable that an alternative hypothesis be specified (Goodman & Royall, 1988). Paradoxes arise when researchers interpret $p$-values as evidence. For instance, results that are surprising under the null may be equally surprising under a plausible alternative hypothesis, such that a $p=.045$ result (`reject the null') does not make the null any less plausible than it was before. Hence, $p$-values have been argued to overestimate the evidence against the null hypothesis. Conversely, it can be the case that statistically non-significant results (i.e., $p>.05)$ nevertheless provide some evidence in favor of the alternative hypothesis. It is therefore crucial for researchers to know when statistical significance and evidence collide, and this requires that a direct measure of evidence is computed and presented alongside the traditional $p$-value.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
