The Problem of Multiple Testing
The Problem of Multiple Testing
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DOI：
10.1016/j.pmrj.2009.10.004
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发表时间：
2009-12-01
期刊：
影响因子：
2.1
通讯作者：
Sainani, Kristin L.
中科院分区：
文献类型：
--
作者：
Sainani, Kristin L.
偶然出现的假阳性结果在医学文献中很常见[1-3]。偶然的是，每个研究样本都会有轻微的不平衡，而不是反映整个人群。如果研究人员观察给定样本的足够特征，他们必然会发现这些怪癖，并(错误地)得出结论，它们对整个人群都有意义。这就是多重测试的问题--对一个样本进行的测试越多，机会发现的可能性就越大。本文将正式描述多重测试的问题，并为读者提供在文献中发现机会发现的工具。
False-positive results that arise as the result of chance are common in the medical literature [1-3]. By chance, every study sample will have slight imbalances that don’t reflect the whole population. If researchers look at enough characteristics of a given sample, they are bound to discover these quirks and conclude (mistakenly) that they have significance for the whole population. This is the problem of multiple testing—the more tests you run on a sample, the greater the likelihood of a chance finding. This article will formally describe the problem of multiple testing and give readers tools for spotting chance findings in the literature.