Hi Rene,
RB>The computation of errors in TProfile has evolved with time
RB>in case of bins with low statistics (1, 2 or 3 entries).
RB>In version 3.02/07, we introduced a new condition, in case
RB>the errors computed are very small (error/content <1e-6)
RB>We had complaints from several users making fits on such profiles
RB>and finding that too much weight was given to the points
RB>with low statistics.
RB>Any idea to improve the existing algorithm is welcome.
as an old PAW user, I know that the error calculation within PAW has always
been the source of major trouble.
Now, errors calculated for histograms have a meaning (usually). Making fits, I
need to be sure that the errors calculated for histograms are representative
of the data. If in a certain bin in profile histograms there are only few
entries, the error is going to be large (usually), independent of spread
option or error-on-the-mean option.
Now, when the error is large, the point will get a low weight in a fit, that's
just how fits are set up to function. Therefore I can hardly understand how it
can happen that such data points get large weights.
Introducing a fix or a fudge every time someone complains does not seem to be
a good policy; in the end we (as users) want to make serious physics with ROOT
and need to be able to rely on such basic things like error calculation.
I do not see, why the spread option and the error-on-the-mean options shold
not suffice. They are meaningful and very (really!!!!) useful.
just my .02$
Martin
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