中文摘要：

目的针对配对样本（单样本）设计、两个和多个独立样本设计的定量资料，分析参数方法、非参数方法以及基于秩转换类参数方法在资料不满足参数方法条件下的适用情形。方法介绍基于秩转换类参数方法的原理及其与非参数方法的关系，采用Monte—Carlo模拟方法，考虑正态和左偏态两种分布，方差齐与不齐两种情形，比较三种方法的I类错误率和检验效能。结果左偏态分布时，无论方差是否齐性，或不涉及方差齐性（单样本设计），参数方法的I类错误率偏离设定水准且明显大于非参数方法和基于秩转换类参数方法，而检验效能明显低于其他两种方法。方差不齐且正态分布时，参数方法的统计性能明显优于其他两种方法。非参数方法和基于秩转换类参数方法在不同资料类型下的统计性能相近。结论基于秩转换类参数方法与非参数方法条件性能相近，适用于非参数方法处理的数据。

英文摘要：

Objective To explore the practicability of parametric test, nonparametric test and parametric test based on rank transformation for quantitative data of paired-sample（one-sample） designs, two independent sample designs as well as three or more independent sample designs when data violate normality or homoscedasticity. Methods Introducing the theory of para- metric test based on rank transformation and comparing type I error and power of the three kind methods by means of Monte Carlo Simulation considering that data are normality or negative skewness and homoscedasticity or heteroscedasticity. Results The results indicate that parametric test contributed to type I error inflations, of which type I error are clearly greater than non- parametric test and parametric test based on rank transformation no matter whether homoscedasticity or not. Parametric test is su- perior to two others when data are normality but heteroscedasticity. Nonparametric test has a good consistency to parametric test based on rank transformation in different designs. Conclusion Type I error and power of parametric test based on rank transfor- mation is nearly equal to that of the nonparametric test when data are applicable to nonparametric test.