Hi Matt, 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 like your analogy to airline tickets...that is something like the situation we seem to have. --Scott Calvin Sarah Lawrence College
Hi Scott,
Would it make sense to define a factor between the size of the two (or more) particles, say "sizeA_v_sizeB" and vary *that* in a fit of the two particles, keeping all the other things (k-weight, ranges, etc) the same for the two fits? That is, instead of asking "what is the size of particle A and what is the size of particle B?", ask "what is the size of particle A and how much bigger is particle B?". If you're observations are right, sizeA_v_sizeB should be statistically different from 1.
--Matt
PS: The price of airline tickets vary widely with many factors, but for any given flight, the price (starting "retail" price) of a first class ticket is always higher than a coach ticket. Of course, the coach ticket on some flights can easily be twice the price of a first class ticket on other flights. But everyone knows first class is always more expensive than coach. ;).