A power fallacy
A power fallacy
复制标题
DOI：
10.3758/s13428-014-0517-4
复制
发表时间：
2015-12-01
影响因子：
5.4
通讯作者：
Morey, Richard D.
中科院分区：
文献类型：
--
作者：
Wagenmakers, Eric-Jan;Verhagen, Josine;Morey, Richard D.
威力谬误指的是一种误解，即在一系列假设实验中，平均适用的东西对每个案例也适用。根据这一谬误，高功率实验总是比低功率实验产生更多的信息数据。在这里，我们用具体的例子揭露这一谬误，证明来自高功率实验的特定结果可以完全没有信息，而来自低功率实验的特定结果可以提供高度的信息。虽然能力在计划实验中是有用的，但在从观察到的数据中做出推断时，它却没有那么有用--有时甚至具有误导性。为了从数据中做出推断，我们建议使用似然比或贝叶斯因子，这是似然比在点假设之外的扩展。这些推论方法并不是对实验的假设重复进行平均，而是以实际观察到的数据为条件。通过这种方式，似然比和贝叶斯因子合理地量化了特定数据集支持或反对零或任何其他假设的证据。
The power fallacy refers to the misconception that what holds on average - across an ensemble of hypothetical experiments-also holds for each case individually. According to the fallacy, high-power experiments always yield more informative data than do low-power experiments. Here we expose the fallacy with concrete examples, demonstrating that a particular outcome from a high-power experiment can be completely uninformative, whereas a particular outcome from a low-power experiment can be highly informative. Although power is useful in planning an experiment, it is less useful-and sometimes even misleading-for making inferences from observed data. To make inferences from data, we recommend the use of likelihood ratios or Bayes factors, which are the extension of likelihood ratios beyond point hypotheses. These methods of inference do not average over hypothetical replications of an experiment, but instead condition on the data that have actually been observed. In this way, likelihood ratios and Bayes factors rationally quantify the evidence that a particular data set provides for or against the null or any other hypothesis.