Gaddis M L
Acad Emerg Med. 1998 Mar;5(3):258-65. doi: 10.1111/j.1553-2712.1998.tb02624.x.
Medical research frequently involves the statistical comparison of >2 groups, often using data obtained through the application of complex experimental designs. Fortunately, inferential statistical methodologies exist to address these situations. Analysis of variance (ANOVA) in its many forms is used to simultaneously test the equality of all groups in a study. One-way (with 1 independent variable), 2-way (with 2 independent variables), and repeated-measures (patients serve as their own controls) ANOVAs are forms of this technique. Each form has been developed to analyze data from a specific experimental design. Analysis of covariance (ANCOVA) allows the researcher to control for confounding variables that may influence the response of the dependent variable. Finally, multivariate analysis of variance (MANOVA) evaluates the simultaneous responses of multiple dependent variables to > or = 1 independent variable. Whereas ANOVA is the correct alternative to statistically inappropriate multiple t-tests, MANOVA is the correct alternative to statistically inappropriate multiple univariate ANOVA calculations. Use of each of these statistical methods requires an appropriate experimental design and data meeting a number of assumptions. When used properly, each of these methods provides a powerful statistical analysis technique.
医学研究常常涉及对两组以上数据的统计比较，这些数据通常是通过复杂的实验设计获得的。幸运的是，存在一些推断统计方法来处理这些情况。方差分析（ANOVA）有多种形式，用于同时检验研究中所有组的均值是否相等。单向方差分析（有1个自变量）、双向方差分析（有2个自变量）和重复测量方差分析（患者自身作为对照）都是这种技术的形式。每种形式都是为分析特定实验设计的数据而开发的。协方差分析（ANCOVA）使研究人员能够控制可能影响因变量反应的混杂变量。最后，多变量方差分析（MANOVA）评估多个因变量对一个或多个自变量的同时反应。虽然方差分析是统计上不适当的多个t检验的正确替代方法，但多变量方差分析是统计上不适当的多个单变量方差分析计算的正确替代方法。使用这些统计方法中的每一种都需要适当的实验设计和符合若干假设的数据。如果使用得当，这些方法中的每一种都能提供强大的统计分析技术。
