Thank you very much for your prompt reply. It is clear now.
In this case the reduced Neyman Chi2 ( Chi2/NDF=1.204 >1 in the example) is just telling that the distribution in each bin (Poisson like) cannot be approximated with a Gaussian. So to evaluate the goodness-of-fit, it is more appropriate the use of reduced Baker-Cousins Chi2 (1.024 ~=1 ,in the example). Is this correct?
I have some set of (simulated) data for which I obtained exactly this behaviour.
Now I have a related question:
I have other similar set of data where the results are not the expected one.
In these cases the percentage of empty bins in these (always simulated) data is around 30%.
Performing the Baker-Cousins Chi2 as 2* MinFCN and dividing for NDf (calculated including also all the zero value-entries of the histogram in the number of entries)
I obtain a reduced Baker-Cousins Chi2 1.14 >1 .
If instead I calculate the reduced Neyman Chi2 , dividing for NDf (not including this time in the calculation all the zero value-entries of the histogram) I obtain
a value of 1.07.
I used for example the command :
TFitResultPtr r47= run47->Fit(“f2”,“L,E,S,M”,“SAME”,0.2,231);
(I tried also without option E,M but I get the same results for MinFCN, Chi2 and parameters)
I know that I didn’t provide many details, but can we exclude that the problem of high reduced Baker-Cousins Chi2 is due to the high number of empty bins and the associated error ?
I thank you very much again for your support.