statistics - Choice of statistical test (in R) of two apparently different distributions -

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i have following list of data each has 10 samples. values indicate binding strength of particular molecule.

what want show 'x' statistically different 'y', 'z' , 'w'. if @ x has more values greater 0 (2.8,1.00,5.4, etc) others.

i tried t-test, of them shows insignificant difference high p-value.

what's appropriate test that?

below 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 way expect samples differ. 1 typically assumes mean mean differs across samples. in case, t-test appropriate. while x has high values, has low values pull mean in. seems thought significant difference (visually) larger variance.

if question variance, need f-test.


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