I've been learning stats on the side and have some difficulty understanding the different methods of comparing means.

I used R and would have posted in their forum, except this is more theory. When I used the anova function on a data set, I got a low p-value and high F - which I understand is a good thing.

Df Sum Sq Mean Sq F value Pr(>F)

X1 4 1594.9 398.7 40.79 2.27e-10 ***

Residuals 24 234.6 9.8

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When this worked out, I looked at TukeyHSD to explain the variation between all possible pairs of means. However, I got high P-Values.

Tukey multiple comparisons of means

95% family-wise confidence level

Fit: aov(formula = X64.5 ~ X1, data = sugar)

$X1

diff lwr upr p adj

Root Borer-Control -21.6500000 -27.227351 -16.072649 0.0000000

Stem Borer-Control -17.8000000 -23.377351 -12.222649 0.0000000

Termites-Control -17.3833333 -22.960685 -11.805982 0.0000000

TopShoot Borer-Control -19.9333333 -25.510685 -14.355982 0.0000000

Stem Borer-Root Borer 3.8500000 -1.467796 9.167796 0.2392782

Termites-Root Borer 4.2666667 -1.051129 9.584462 0.1600002

TopShoot Borer-Root Borer 1.7166667 -3.601129 7.034462 0.8738684

Termites-Stem Borer 0.4166667 -4.901129 5.734462 0.9993230

TopShoot Borer-Stem Borer -2.1333333 -7.451129 3.184462 0.7613845

TopShoot Borer-Termites -2.5500000 -7.867796 2.767796 0.6257874

The P-values are much higher than 0.05. Does this mean I should reject these tests. Is this because of the 95% confidence limit? Thanks a ton