Dear experts,

I’m trying to fit a weighted MC sample with RooFit, Root version 6.24. If no option is specified for the error calculation in the fitTo(), I receive the following warning:

```
[#0] WARNING:InputArguments -- RooAbsPdf::fitTo(pdf_fit) WARNING: a likelihood fit is requested of what appears to be weighted data.
While the estimated values of the parameters will always be calculated taking the weights into account,
there are multiple ways to estimate the errors of the parameters. You are advised to make an
explicit choice for the error calculation:
- Either provide SumW2Error(true), to calculate a sum-of-weights-corrected HESSE error matrix
(error will be proportional to the number of events in MC).
- Or provide SumW2Error(false), to return errors from original HESSE error matrix
(which will be proportional to the sum of the weights, i.e., a dataset with <sum of weights> events).
- Or provide AsymptoticError(true), to use the asymptotically correct expression
(for details see https://arxiv.org/abs/1911.01303).
```

and the fit converges [covQual() = 3]. The same happens if I use SumW2Error(false). However, if I use the option SumW2Error(true), the fit doesn’t converge [covQual() = 2]. Also, in any of these configurations, the yield’s uncertainty returned is too small [much smaller than sqrt(N)]; for example, with SumW2Error(false) I get

`Nsig = 731393 +- 34.7383, covQual() = 3`

and with SumW2Error(true) I get

`Nsig = 731393 +- 38.0404, covQual() = 2`

.

I also tested with another minimizer (Minuit2), but the results are very similar.

My questions are:

- Why is the fit failing to converge with SumW2Error(true)?
- And why is the fitted yield’s uncertainty so small in both cases?

A short running example with the whole code and a rootfile is attached in case you want to reproduce this problem.

Thanks in advance!

Cheers,

FitG2CB_simple_MC.C (11.4 KB)

histofile_MC.root (5.5 KB)