机器算法验证 - Tukey HSD 测试如何比 t.test 的未校正 P 值更显着？ - 吾爱随笔录

我是通过帖子“双向方差分析的事后配对比较”（回复这篇文章）来的，它显示了以下内容：

dataTwoWayComparisons <- read.csv("http://www.dailyi.org/blogFiles/RTutorialSeries/dataset_ANOVA_TwoWayComparisons.csv")

model1 <- aov(StressReduction~Treatment+Age, data =dataTwoWayComparisons)
summary(model1) # Treatment is signif

pairwise.t.test(dataTwoWayComparisons$StressReduction, dataTwoWayComparisons$Treatment, p.adj = "none")
# no signif pair

TukeyHSD(model1, "Treatment")
# mental-medical   is the signif pair.

（输出附在下面）

有人可以解释一下为什么 Tukey HSD 能够找到一个重要的配对，而配对（未调整的 pvalue）t 检验却没有这样做吗？

谢谢。

这是代码输出

> model1 <- aov(StressReduction~Treatment+Age, data =dataTwoWayComparisons)
> summary(model1) # Treatment is signif
            Df Sum Sq Mean Sq F value    Pr(>F)    
Treatment    2     18   9.000      11 0.0004883 ***
Age          2    162  81.000      99     1e-11 ***
Residuals   22     18   0.818                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
> 
> pairwise.t.test(dataTwoWayComparisons$StressReduction, dataTwoWayComparisons$Treatment, p.adj = "none")

        Pairwise comparisons using t tests with pooled SD 

data:  dataTwoWayComparisons$StressReduction and dataTwoWayComparisons$Treatment 

         medical mental
mental   0.13    -     
physical 0.45    0.45  

P value adjustment method: none 
> # no signif pair
> 
> TukeyHSD(model1, "Treatment")
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = StressReduction ~ Treatment + Age, data = dataTwoWayComparisons)

$Treatment
                 diff         lwr        upr     p adj
mental-medical      2  0.92885267 3.07114733 0.0003172
physical-medical    1 -0.07114733 2.07114733 0.0702309
physical-mental    -1 -2.07114733 0.07114733 0.0702309

> # mental-medical   is the signif pair.