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To test or not to test: Preliminary assessment of normality when comparing two independent samples

机译:测试或不测试:比较两个独立样本时的初步正常性评估

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Background Student’s two-sample t test is generally used for comparing the means of two independent samples, for example, two treatment arms. Under the null hypothesis, the t test assumes that the two samples arise from the same normally distributed population with unknown variance. Adequate control of the Type I error requires that the normality assumption holds, which is often examined by means of a preliminary Shapiro-Wilk test. The following two-stage procedure is widely accepted: If the preliminary test for normality is not significant, the t test is used; if the preliminary test rejects the null hypothesis of normality, a nonparametric test is applied in the main analysis. Methods Equally sized samples were drawn from exponential, uniform, and normal distributions. The two-sample t test was conducted if either both samples (Strategy I) or the collapsed set of residuals from both samples (Strategy II) had passed the preliminary Shapiro-Wilk test for normality; otherwise, Mann-Whitney’s U test was conducted. By simulation, we separately estimated the conditional Type I error probabilities for the parametric and nonparametric part of the two-stage procedure. Finally, we assessed the overall Type I error rate and the power of the two-stage procedure as a whole. Results Preliminary testing for normality seriously altered the conditional Type I error rates of the subsequent main analysis for both parametric and nonparametric tests. We discuss possible explanations for the observed results, the most important one being the selection mechanism due to the preliminary test. Interestingly, the overall Type I error rate and power of the entire two-stage procedure remained within acceptable limits. Conclusion The two-stage procedure might be considered incorrect from a formal perspective; nevertheless, in the investigated examples, this procedure seemed to satisfactorily maintain the nominal significance level and had acceptable power properties.
机译:背景技术学生的两样本t检验通常用于比较两个独立样本(例如两个治疗臂)的平均值。在原假设下,t检验假设两个样本来自相同的正态分布总体,方差未知。要充分控制I型错误,必须保持正态性假设,通常通过初步的Shapiro-Wilk检验对其进行检验。以下两步程序被广泛接受:如果正常性的初步检验不重要,则使用t检验;如果初步检验拒绝了正态性的零假设,则在主要分析中应用非参数检验。方法从指数分布,均匀分布和正态分布中抽取均等大小的样本。如果两个样本(策略I)或两个样本的残差集合(策略II)均已通过初步的Shapiro-Wilk检验,以进行正态检验,则进行两次样本t检验。否则,进行了Mann-Whitney的U检验。通过仿真,我们分别估计了两阶段过程的参数和非参数部分的条件I型错误概率。最后,我们评估了总体I类错误率和整个两阶段过程的功效。结果正常性的初步测试严重改变了随后的参数和非参数测试主要分析的条件I型错误率。我们讨论了观察结果的可能解释,其中最重要的是由于初步测试而产生的选择机制。有趣的是,整个两阶段程序的总体I类错误率和功效保持在可接受的范围内。结论从正式的角度看,两阶段程序可能被认为是错误的。但是,在所研究的示例中,此过程似乎可以令人满意地保持名义上的显着性水平并具有可接受的功率特性。

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