方差分析 (ANOVA) 是一种统计方法,它将数据集中观察到的总变异性划分为可归因于不同来源的各个部分。其核心思想是比较不同组均值之间的方差与组内方差。如果组间方差显著较大,则表明组均值确实存在差异。

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方差分析 (ANOVA) 是一种统计方法,它将数据集中观察到的总变异性划分为可归因于不同来源的各个部分。其核心思想是比较不同组均值之间的方差与组内方差。如果组间方差显著较大,则表明组均值确实存在差异。
The fundamental principle of ANOVA, conceived by Ronald A. Fisher, revolutionized experimental design. Before ANOVA, researchers often used multiple t-tests to compare several groups, a practice that inflates the Type I error rate (the probability of a false positive). ANOVA provides a single test to check for any difference among group means. The technique works by decomposing the total variation in a dataset, measured by the Total Sum of Squares ([latex]SS_{Total}[/latex]), into two parts. The first part is the Sum of Squares Between groups ([latex]SS_{Between}[/latex]), which measures the variation of each group’s mean from the overall grand mean. This represents the variation explained by the grouping factor. The second part is the Sum of Squares Within groups ([latex]SS_{Within}[/latex]), which measures the variation of each observation from its own group’s mean. This represents the unexplained or random variation, often called error. If the variation between groups is substantially larger than the variation within groups, it provides evidence that the grouping factor has a significant effect on the outcome variable. This comparison is formalized through the F-statistic.
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方差分割(ANOVA)
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