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首页> 外文期刊>Preventive Medicine: An International Journal Devoted to Practice and Theory >Null misinterpretation in statistical testing and its impact on health risk assessment.
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Null misinterpretation in statistical testing and its impact on health risk assessment.

机译：统计测试中的零误解及其对健康风险评估的影响。

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Statistical methods play a pivotal role in health risk assessment, but not always an enlightened one. Problems well known to academics are frequently overlooked in crucial nonacademic venues such as litigation, even though those venues can have profound impacts on population health and medical practice. Statisticians have focused heavily on how statistical significance overstates evidence against null hypotheses, but less on how statistical nonsignificance does not correspond to evidence for the null. I thus present an example of a highly credentialed statistical expert conflating high "nonsignificance" with strong support for the null, via misinterpretation of a P-value as a posterior probability of the null hypothesis. Reverse-Bayes analyses reveal that nearly all the support for the null claimed by the expert must have come from the expert's prior, rather than the data, there being no background data that could support a strong prior. The example illustrates how inattention to the actual meaning of P-values and confidence limits allow extremely biased prior opinions (including null-spiked opinions) to be presented as if they were objective inferences from the data.
机译：统计方法在健康风险评估中起着举足轻重的作用，但并非总是一种开明的方法。尽管在一些重要的非学术场合，例如诉讼，学术界众所周知的问题经常被忽视，尽管这些场所可能对人口健康和医疗实践产生深远影响。统计学家主要集中在统计意义如何夸大针对无效假设的证据，而较少关注统计无意义如何与无效证据相对应。因此，我通过一个将P值误认为是零假设的后验概率的例子，来证明一个高度认可的统计专家将高“非重要性”与对零的强烈支持相结合的例子。反向贝叶斯分析表明，专家声称的对null的几乎所有支持都必须来自专家的先验，而不是数据，没有背景数据可以支持强先验。该示例说明了对P值和置信度限制的实际含义的漠不关心如何使极端偏颇的先验观点（包括零散观点）能够被呈现，就好像它们是来自数据的客观推断一样。

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