On Sat, 16 Aug 2014 10:50:30 -0700 (PDT), email@example.com wrote:
>Hi, > >We are testing some attribute of pairs of objects that satisfy a >given crierion vs. pairs that do not. However, in the sample we have >all the pairs satisfy the criterion.
You want to test A vs. B; and you have no B. Therefore: You do not have any test. That seems straightforward.
Can you make some argument about how that test would result, if data existed for it? ... conceivably. I would think of depending on observably high (near perfect?) correlations. - That, however, would (it seems to me, though I have no concrete example) rule out the strategy that you go on to propose. Assuming that I understand your proposal at all.
> I proposed randomly paring the objects repeatedly and check the >attribute. The permutation would be w/o restriction. It would allow >pairs that satisfy as well as pairs that do not satisfy the criterion. >Some members of our group claim that we should exclude from the random >permutations the pairs that satisfy the criterion, keep randomizing >untill none of the observed pairs appears in the permutation. Is this >an accespted approach? My recollection is that in a permutation test >the observed data must be a possible outcome. Could you plese point me >to any reference that explaints which way is correct? >
Nope, I don't follow; but I'm not trying very hard. Neither the data description nor the strategy rings any bells of familiarity.
Maybe this all makes sense when you plug in the concrete terms, but I'm not intrigued enough to fiddle with my own made-up examples (which are unlikely to be anything like your data).