Hi Scott,
I tried that, before realizing it didn't really do anything. Performing a multi-dataset fit with two guessed variables, say size_A and sizeA_v_sizeB, is of course completely equivalent to just having the two sizes as guessed variables. And if size_A is set to some arbitrary (but reasonable) value, sizeA_v_sizeB is equivalent to just fitting size_B, and produces the "absolute" uncertainty again. This would be different if the fit were truly multi-dataset in the sense that we had parameters in common between samples, so that constraining the size of A had some effect on the fit for B. But the parameters that are in common, like S02, we constrained to a standard rather than refining through a multi-dataset fit.
I think (I think) the goal would be to know whether particle A was always larger than particle B (or vice versa). In that case, analyzing data for the two sets together should help: If you allow sizeA and sizeB to be adjusted in the fit, you may well get uncertainties that overlap, but will also get a measure of their correlation. Like you say, Adjusting sizeA and the size ratio would not change the final result, but it would put the emphasis on knowing the correlation, which I think is what you want. But I think that this might mean the multi-dataset fit would have to include S02 and other common parameters. It might also need to include some of the changes in k-weight, ranges, etc that you alluded to earlier. Basically it would mean looking for the correlation of the sizes. --Matt For completeness: Let's say you have sizeA = 15+/-4 and sizeB = 17+/-4. If sizeA and sizeB have a large, positve correlatation, increasing sizeA from 15 would mean that sizeB would have to increase from 17. A large, negative correlation would mean sizeB would have to *decrease* from 17, and a small correlation would mean that sizeA could change without necessarily causing any significant change in sizeB. In the airline ticket analogy, a survey of coach and first class ticket prices mighty give a difference in average prices that was smaller than the standard deviation in each price. The prices are positively and highly correlated, but to know that, you have to record both prices for each flight.