【作者】 曹明芹；

【导师】 薛茜；

【作者基本信息】 新疆医科大学 ， 卫生统计学， 2004， 硕士

【摘要】 目的 对完全随机设计两样本比较常用方法t检验、u检验、Satterthwaite法,Cochran-cox法和Wilcoxon秩和检验等方法进行评价,为实际工作中统计方法的合理选择提供参考和依据。 方法用SAS软件编程,在计算机上模拟不同总体分布、参数相等或不等及样本含量相等或不等条件下,分别采用五种不同方法进行假设检验,评价它们的Ⅰ型误差估计值EⅠ、Ⅱ型误差估计值EⅡ及检验效率。结果t检验对方差不齐较敏感;样本含量较小时使用U检验,EⅠ偏大;当总体分布为非正态分布样本含量较小时,使用Satterthwaite法,Cochran-cox法检验EⅠ偏大。总体分布为偏态分布形状相同时或者总体分布为L型分布时,Wilcoxon秩和检验的EⅡ最小:Wilcoxon秩和检验的效能并不低于t检验,甚至有时还高于t检验;当两样本含量均较大时,五种方法的检验效能较接近。 结论 参数检验的效能不一定高于非参数检验,在非参数检验方法不断发展的今天,应在适当的情况下,选择非参数统计方法,可以较好地解决实际问题。在两样本含量均较大时,t检验、u检验、Satterthwaite法,Cochran-cox法和Wilcoxon秩和检验等方法相互替代是可行的。并提出临床收集资料和合理选择统计方法的建议。更多还原

【Abstract】 Object Evaluation of the comparative methods of independent samples: Mest, u-test, Satterthwaite method, Cochran-cox method and Wilcoxon rank-sum test, offers some information for statistical analysis in practice. Methods All experiments was simulated by SAS software with different population distribution, different parameters and different sample sizes. Using the type I error, the type II error and test efficiency of five methods as the index of evaluation. Results The t-test is sensitive to unequal population variance, u-test is appropriate to large samples comparative. Satterthwaite method and Cochran-cox method are sensitive to skewed distribution with small sample sizes. When two population’s shape are same or two population are L-distribution, the type II error of Wilcoxon rank-sum test is smallest than other tests. The power of Wilcoxon rank-sum test is a little less than that of Mest, Furthermore it is higher than Mest sometimes. Conclusion The experiments express that the power of parametric test is not higher than nonparametric test at any time. According to different situation to select different statistical analysis methods in order to interpret data reasonably in practice. When sample size is large enough, Mest, u-test, Satterthwaite method, Cochran-cox method and Wilcoxon rank-sum test are all recommended. The author give some suggest for sorting data and selecting the comparative methods of independent samples.更多还原