I am performing an unbinned ML fit of weighted MC, where a RooAbsPdf is fitted to a weighted RooDataSet and the average weight is small with a value of about 1/333. The command looks like this:
The resulting errors on the fitted pdf parameters are much too large. When fitting the unweighted data set the parameter errors drop more than an order of magnitude. This problem also occurs when adding the option RooFit::SumW2Error(kTRUE) or without using Minos.
I am using root version 5.34.10.
The same issue has also been reported in this unresolved post
Is there a way to fit a weighted data set and obtain a correct error estimate on the fitted parameters ?
Can you please post the code reproducing this problem ? Bare in mind that using weights is just an approximation, which in some case it might not be correct, in particular if the weights are very different from each others (i.e. some very small and some very large). In that case one would need the full weight distribution and/or have the right model.