Significance Testing Needs a Taxonomy: Or How the Fisher, Neyman-Pearson Controversy Resulted in the Inferential Tail Wagging the Measurement Dog
Significance Testing Needs a Taxonomy: Or How the Fisher, Neyman-Pearson Controversy Resulted in the Inferential Tail Wagging the Measurement Dog
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DOI：
10.1177/0033294116662659
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发表时间：
2016-10-01
影响因子：
2.3
通讯作者：
Brand, Andrew
中科院分区：
文献类型：
--
作者：
Bradley, Michael T.;Brand, Andrew
准确的测量和带有推论统计的截止概率并不完全兼容。费舍尔明白这一点，当时他开发了F检验来处理测量的可变性，并对可能值得进一步研究的操纵做出判断。Neyman和Pearson专注于参数高度确定的建模分布，并得出结论，在F检验之后可以准确地做出推断判断，因为分布参数是确定的。内曼和皮尔逊的方法在使用阿尔法和贝塔错误率的统计分析中发挥了主导作用，在高度确定的情况下适当地指导推理判断，在科学探索中也不适当。费舍尔试图解释不同的情况，但部分由于一些晦涩的措辞，引发了长期的争议，目前已经离开了费舍尔p的重要性
Accurate measurement and a cutoff probability with inferential statistics are not wholly compatible. Fisher understood this when he developed the F test to deal with measurement variability and to make judgments on manipulations that may be worth further study. Neyman and Pearson focused on modeled distributions whose parameters were highly determined and concluded that inferential judgments following an F test could be made with accuracy because the distribution parameters were determined. Neyman and Pearson's approach in the application of statistical analyses using alpha and beta error rates has played a dominant role guiding inferential judgments, appropriately in highly determined situations and inappropriately in scientific exploration. Fisher tried to explain the different situations, but, in part due to some obscure wording, generated a long standing dispute that currently has left the importance of Fisher's p