Alphas, betas and skewy distributions: two ways of getting the wrong answer.
Alphas, betas and skewy distributions: two ways of getting the wrong answer.
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DOI：
10.1007/s10459-011-9283-6
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发表时间：
2011-08
影响因子：
4
通讯作者：
Fayers, Peter
中科院分区：
文献类型：
--
作者：
Fayers, Peter
尽管许多参数统计检验被认为是稳健的，正如最近在方法论者角落中所显示的那样，但对统计检验背后的假设仍然需要谨慎。在本文中，我表明鲁棒性主要指α，即第一类误差。如果忽略数据的基本分布，则可能会在 β（II 类错误）方面遭受重大损失，代表假阴性率大幅增加，或者相当于测试功效的严重损失。
Although many parametric statistical tests are considered to be robust, as recently shown in Methodologist’s Corner, it still pays to be circumspect about the assumptions underlying statistical tests. In this paper I show that robustness mainly refers to α, the type-I error. If the underlying distribution of data is ignored there can be a major penalty in terms of the β, the type-II error, representing a large increase in false negative rate or, equivalently, a severe loss of power of the test.
影响因子：
4
作者：
Norman, Geoff
通讯作者：
Norman, Geoff