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Just a note
In pooling samples A&B you force the nil hypothesis to be true. So, provided no two values in A and B are the same, if you were to randomly class one of that pooled set of values as 'A' and class another as 'B', you are equally likely to observe A>B as B>A. In which case AB is equally likely to be positive as negative. In other words P(A>B) = P(B>A) = 0.5. Similarly, we would expect the median difference between those pairs of randomly selected values, M(AB), will equal zero. Expressed as a parameter of the null model, this HodgesLehmann difference, HLδ = 0.
If however we randomly select one value from sample A and compare it with a randomly selected value from sample B, and repeat that enough times, we are likely to find P(A>B) differs substantially from 0.5 and M(AB) is not 0. In that case their observed median difference, HLd, will deviate from HLδ. Of course the null model assumes values from the null population are assigned to samples A&B at random so, if that model is correct, any difference between HLd and HLδ has arisen by simple chance. The WMW test assesses the probability, under this nil hypothesis, of observing a value as or more deviant than the observed HLd.
