Gaddis G M, Gaddis M L
Department of Emergency Health Services, Truman Medical Center, University of Missouri-Kansas City School of Medicine 64108.
Ann Emerg Med. 1990 Jul;19(7):820-5. doi: 10.1016/s0196-0644(05)81712-3.
Statistical methods used to test the null hypothesis are termed tests of significance. Selection of an appropriate test of significance is dependent on the type of data to be analyzed and the number of groups to be compared. Parametric tests of significance are based on the parameters, mean, standard deviation, and variance, and thus are used appropriately when interval or ratio data are analyzed. The t-test and analysis of variance (ANOVA) are examples of parametric tests of significance. Assumptions regarding the data to be analyzed when using the t-test or ANOVA include normality of the populations from which the sample data are drawn, homogeneity of the variances of the populations from which the sample data are drawn, and independence of the data points within a sample group. The t-test is the appropriate test of significance to use if there are only two groups to compare. If there are three or more groups to compare, ANOVA is the appropriate test. ANOVA holds the preset alpha level constant. While ANOVA will imply a significant difference between the groups compared, a multiple comparison test will define which of the three or more groups differ significantly.
用于检验原假设的统计方法被称为显著性检验。选择合适的显著性检验取决于要分析的数据类型和要比较的组数。参数显著性检验基于均值、标准差和方差等参数，因此在分析区间数据或比率数据时适用。t检验和方差分析（ANOVA）是参数显著性检验的例子。使用t检验或ANOVA时，关于要分析的数据的假设包括样本数据所来自总体的正态性、样本数据所来自总体的方差齐性以及样本组内数据点的独立性。如果只有两组要比较，t检验是合适的显著性检验。如果有三组或更多组要比较，ANOVA是合适的检验。ANOVA保持预设的α水平不变。虽然ANOVA会表明所比较的组之间存在显著差异，但多重比较检验将确定三组或更多组中哪些组存在显著差异。
