I want to know how the errors are calculated by ROOT histo.
I have checked the classes responsible for the error, where I find that the the size of the error bars is equal to the square root of the number of entries in that histogram bin. That is, if a histogram bin contains “N” no. of entries the the error associated is sqrt(N). But in that case more statistics impliy more errors, which is unphysical.
But as per my understanding 1/sqrt(N) should be the error. Please let me know whether I am wrong ???
My second quary is about the option h->Sumw2();
" which means “sum the squares of the weights”" - could you please elaborate this line which I got from the manual. And in which situation should I use this option.
If the histogram represent counts, thus when each entry that is filled has a weight=1, the statistical error in each bin is computed as SQRT(N), where N is the number of entries in each bin, which by definition is in this case equal to the bin content.
If you are filling the histogram with a weight different than one, then the bin content is equal to the sum of the weight in each bin (different than the number of entries in each bin).
The statistical error in this case con be approximately computed from SQRT( sum of weight^2) in each bin.
However, in order to have the histogram doing this, you ned to set the option TH1::Sumw2().
By doing this the histogram will store the bin sum of weight square, that can be used to compute the errors.
So you should use this option when your histogram is filled with weights different than one.