Well, I assume your errors scale as sqrt(entries) so, if you want to reduce errors by a factor 3 (4, 5, …), you would need 9 (16, 25, …) times more events.
Note that there exists another method which optionally computes binomial errors: TH1::Divide (const TH1 *h1, const TH1 *h2, Double_t c1=1, Double_t c2=1, Option_t *option=“”)
And maybe you should try (it allows several different errors’ treatment methods): TRatioPlot
Hi,
How are you confident that the error is too large or too small ?
Do you know the probability distribution of the observed bin content ? What the bin content represents for you ?
If the bin content represent counts, and the histogram normalisation is not fixed, the distribution is a Poisson and the estimated error in ROOT is the one assuming the true value of the Poisson is the observed bin count.
If you are filling the histogram with weighted counts, then an approximated procedure is us…
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