In a laboratory, two researchers have anayzed the same data from a two-way experiment with the following variables: genotype (Wildtype (WT) vs Knockout (KO)) , and treeatment (Control or Treatment). This data is available in the file factcrossing.csv.

The first researcher has used a two-way ANOVA, followed by a TukeyHSD, and obtained the following results:

> summary(data.aov)
Df Sum Sq Mean Sq F value Pr(>F)
genotype           1    2.7    2.72   0.055  0.818
treatment          1   35.9   35.87   0.720  0.409genotype:treatment 1   16.9   16.93   0.340  0.568Residuals         16  797.2   49.83
> TukeyHSD(data.aov)   Tukey multiple comparisons of means     95% family-wise confidence level Fit: aov(formula = measure ~ genotype * treatment, data = data)

$genotype diff lwr upr p adj KO-WT0.7378864 -5.95428 7.430053 0.8181487$treatment
$genotype:treatment diff lwr upr p adj KO:C-WT:C 2.5779733 -10.194827 15.35077 0.9374098 WT:T-WT:C 4.5183786 -8.254422 17.29118 0.7448413 KO:T-WT:C 3.4161779 -9.356623 16.18898 0.8688435 WT:T-KO:C 1.9404052 -10.832395 14.71321 0.9716065 KO:T-KO:C 0.8382046 -11.934596 13.61101 0.9975520 KO:T-WT:T -1.1022006 -13.875001 11.67060 0.9944969  The other researcher was only interested in the difference (within the controls), between the WT and KO. He decided to use a t-test comparing these groups: > t.test(data$measure[data$treatment=="C"] ~ data$genotype[data$treatment=="C"]) Welch Two Sample t-test  data: data$measure[data$treatment == "C"] by data$genotype[data\$treatment == "C"]