If your data are distributed normally, the t-test can help you determine if the means of your two samples are greater than or less than each other (one-tailed) or just plain different from each other (two-tailed). These examples are for paired samples, to make them not paired just delete the "paired=TRUE" statement altogether, default is unpaired.
We'll use the same data throughout:
x <- c(1.83, 0.50, 1.62, 2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
y <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
When using your own data you just need them in vectors (as shown).
One-tailed
t.test(x,y,paired=TRUE,alternative="greater")
t.test(x,y,paired=TRUE,alternative="less")
Two-tailed
t.test(x,y,paired=TRUE)
What if your data are not normal? The non-parametric Wilcoxon test will do the trick. The Wilcoxon rank-sum test is for independent samples (unpaired). The Wilcoxon signed rank test is for paired samples.
wilcox.test(x, y, paired = TRUE)
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