Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.
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DOI：
10.1007/s10654-016-0149-3
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发表时间：
2016-04
影响因子：
13.6
通讯作者：
Altman DG
中科院分区：
文献类型：
--
作者：
Greenland S;Senn SJ;Rothman KJ;Carlin JB;Poole C;Goodman SN;Altman DG
几十年来，对统计检验、置信区间和统计功效的误解和滥用一直受到谴责，但仍然猖獗。一个关键的问题是，对这些概念的解释并不简单、直观、正确和简单。相反，正确使用和解释这些统计数据需要注意细节，这似乎会增加工作科学家的耐心。这种高认知需求导致了一种流行病，即简单的定义和解释是错误的，有时甚至是灾难性的，然而这些错误的解释主导了大部分的科学文献。鉴于这一问题，我们提供的定义和讨论的基本统计数据是更一般和关键比通常发现在传统的介绍性论述。我们的目标是为统计理论和技术的知识可能有限但希望避免和发现误解的教师，研究人员和统计消费者提供资源。我们强调，违反通常未说明的分析协议（如选择分析的基础上，他们产生的P值）可能会导致小P值，即使声明的检验假设是正确的，并可能导致大P值，即使该假设是不正确的。然后，我们提供了一个解释性的列表，其中包括25个对P值、置信区间和功效的误解。最后，我们提出了改进统计解释和报告的指导方针。
Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so—and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.