Dear expert, I am using ROOT to draw histograms with statistics errors. I know that TH1::Sumw2() can store and calculate errors. However I don’t understand why the formula of Sumw2() is like this: sqrt(sum of squared weight). For example, if I fill a bin with 1 for 10 times, the error ROOT gives …

Maybe you are looking for: TH1::GetStdDev

Hi, thanks for your reply. Actually my confusion is that why not use standard variation to calculate the error for each bin. For your suggestion, it seems that TH1::GetStdDev is for the whole histogram, not for each bin. So that is not what I am concerned now.

I’m afraid you need to look into some statistics book then (search for “random / statistical errors”).

Hi, thanks for your reference. After reading it, I got to know the definition of statistics error, or random error. However, how can I relate this to Sumw2 in ROOT? For example, if I fill a bin with weight 10, then ROOT gives that bin content is 10 and bin error is also 10 if I call Sumw2(). Howeve…

Hi, Actually I am plotting histograms of dN/dpT in pp and AA simulations. So the bin content represents the yield in each pT bin. So I think it is OK if ROOT also assumes Poisson distribution. For the weighted counts, the “weight” could be energy if I want to plot dE/deta instead of dN/deta. So I …

Hi, This simple blog explains why the bin error for weighted histogram is equal to the square root of their the sum of the bin weight square. https://www-zeuthen.desy.de/~wischnew/amanda/discussion/wgterror/working.html Another derivation is also here http://www.hep.uiuc.edu/e687/memos/weight_er…

Hi, these are exactly what I want. Thanks guys!

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Wile_E_Coyote March 21, 2018, 9:46pm 6

Questions about getting events in each bin/checking error bar is correct or not ROOT

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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