# Two samples of the same distribution

What non-parametric methods exist to assess whether two random samples belong to the same distribution?

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Please consider the quality of your questions and the frequency of asking them. You can't possibly have done much background searching/research yourself on such disparate topics if you are posting 4 questions within 24hours (unless you save up the questions for us ;-) We really aren't here to do your work/research for you. The number of votes for Qs reflects the quality and amount of research effort demonstrated, and your Qs are somewhat lacking in that regard. –  Gavin Simpson Nov 15 '11 at 13:04
I second Gavin's suggestion. We like to help, but not at a rate that people usually get paid for. –  Nick Sabbe Nov 15 '11 at 13:56
@NickSabbe unfortunately here I didn't find nobody that want to get paid. I tried sometime in chat but nothing... I can pay for those questions but there aren't emails –  Dail Nov 15 '11 at 14:10

Non-parametric methods like the Mann-Whitney U test (Wilcoxon rank sum test) test the hypothesis that the two samples' distributions come from populations having the same location (the alternative hypothesis being that the two samples come from populations with different locations, e.g. one distribution has larger values than the other). In R, see ?wilcox.test. The Kruskal-Wallis test (in R, ?kruskal.test) is a more general test for two or more groups.
The Kolmogorov-Smirnov test considers differences in the distributions per se (so is sensitive to differences in location, variance, skewness, etc) of the two candidate distributions. In R, see ?ks.test.