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# st: Using Wilcoxon rank-sum (Mann-Whitney) test to compare an emipirical and a uniform distribution

 From "Tsankova, Teodora" To Subject st: Using Wilcoxon rank-sum (Mann-Whitney) test to compare an emipirical and a uniform distribution Date Thu, 7 Mar 2013 20:11:58 -0000

```Some time ago I posted on statlist with a question regarding the use of a one-sided KS test and I was advised that for my purpose I can use the Wilcoxon-Mann-Whitney test (ranksum command in Stata).

I basically have 15 observations that go from 0 to 1 and constitute my empirical distribution and I want to prove that those take higher values than a uniform distribution would suggest. I have three questions related to the test:

1) I generate myself 15 more observation which take values from 0 to 1 with a constant markup of 1/14 (I simulate a uniform distribution of 15 variables in the same interval). Has anyone else used this method for creating uniform distribution and do you see any problems with it?

2)  I use the ponder option to compute the p-value for the one sided test and I get the following output:

Two-sample Wilcoxon rank-sum (Mann-Whitney) test

ObservedOr~m |      obs    rank sum    expected
-------------+---------------------------------
Observed |       15         236       232.5
Uniform |       15         229       232.5
-------------+---------------------------------
combined |       30         465         465

----------

Ho: ktaub_~m(Observ~m==Observed) = ktaub_~m(Observ~m==Uniform)
z =   0.145
Prob > |z| =   0.8846

P{ktaub_~m(Observ~m==Observed) > ktaub_~m(Observ~m==Uniform)} = 0.516
999996

I would interpret it in the following way: In 51.6% of the cases you would draw a random number from Observed that would be higher than a random draw from Uniform. Is this the correct interpretation?

3) My last question is related to the fact that Wilcoxon Mann-Whitney test is used to analyse ordinal data. My data has an ordinal meaning in the sense higher values represent more homogenous group lending villages in my case. However, the values the variable takes are not interval but continuous ones. Can I still use this test?

Thank you,

Teodora

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