I have the following list of data each has 10 samples. The values indicate binding strength of a particular molecule.
What I want so show is that 'x' is statistically different from 'y', 'z' and 'w'. Which it does if you look at X it has more values greater than zero (2.8,1.00,5.4, etc) than others.
I tried t-test, but all of them shows insignificant difference with high P-value.
What's the appropriate test for that?
Below is my code:
#!/usr/bin/Rscript
x <-c(2.852672123,0.076840264,1.009542943,0.430716968,5.4016,0.084281843,0.065654548,0.971907344,3.325405405,0.606504718)
y <- c(0.122615039,0.844203734,0.002128992,0.628740077,0.87752229,0.888600425,0.728667099,0.000375047,0.911153571,0.553786408);
z <- c(0.766445916,0.726801899,0.389718652,0.978733927,0.405585807,0.408554832,0.799010791,0.737676439,0.433279599,0.947906524)
w <- c(0.000124984,1.486637663,0.979713013,0.917105894,0.660855127,0.338574774,0.211689885,0.434050179,0.955522972,0.014195184)
t.test(x,y)
t.test(x,z)
You have not specified in what way you expect the samples to differ. One typically assumes you mean the mean differs across samples. In that case, the t-test is appropriate. While x has some high values, it also has some low values which pull the mean in. It seems what you thought was a significant difference (visually) is actually a larger variance.
If your question is about variance, then you need an F-test.