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RE: st: Mann-whitney U test


From   "Nick Cox" <[email protected]>
To   <[email protected]>
Subject   RE: st: Mann-whitney U test
Date   Sun, 23 Jan 2005 15:46:09 -0000

In addition to Roger's routines
and remarks, note that the -ranksum- 
command has a fairly recently 
added option, -porder-. 

To underline the view that 
measuring the magnitude of 
something is often much more
interesting and useful than testing 
a null hypothesis, I would argue 
for making it part of the default
output. 

Nick 
[email protected] 

Roger Newson
 
> At 01:25 23/01/2005, Ricardo wrote:
> >Thank Roger. I am familiar with this program and I
> >have used it before. So the test really test both
> >hypotheses: that the difference between the median is
> >zero, and that the degree of non-overlap of the two
> >populations is zero. i.e. whether the degree of
> >overlap between the two populations is significantly
> >different than would be expected by chance alone. Is
> >this correct?
> 
> No and yes. The Wilcoxon ranksum test does indeed test the 
> hypopthesis that 
> Somers' D is zero, where Somers' D is the difference between 2 
> probabilities, namely the probability that a randomly-chosen 
> member of 
> Subpopulation A has a higher outcome value than a 
> randomly-chosen member of 
> Subpopulation B and the probability that a randomly-chosen member of 
> Subpopulation B has a higher outcome value than a 
> randomly-chosen member of 
> Subpopulation A. If these 2 probabilities are equal, then you 
> can argue 
> that (in Ricardo's words) "the degree of non-overlap of the 
> two populations 
> is zero". However, the Hodges-Lehmann median difference is 
> not always the 
> difference between the 2 subpopulation medians. The 
> Hodges-Lehmann median 
> difference is the median difference between 2 outcome values, 
> assuming that 
> the first is sampled at random from Subpopulation A and the second is 
> sampled at random from Subpopulation B.
> 
> If the 2 sub-population distributions are different only in 
> location, then 
> the Hodges-Lehmann median difference is indeed the difference 
> between the 2 
> subpopulation medians, because then the difference between 2 
> outcome values 
> sampled independently from the 2 subpopulations is distributed 
> symmetrically around the location difference, and the median 
> difference is 
> the mean difference, which is the difference between means, 
> which is the 
> difference between medians. However, the 2 subpopulations may 
> differ in 
> ways other than location, and then the difference between the 
> 2 medians may 
> be different from the Hodges-Lehmann median difference. I 
> often get queries 
> from users of my program -cendif- (part of the -somersd- 
> package) asking 
> why, in their data, the Hodges-Lehmann median difference is not the 
> difference between the 2 medians.
> 

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