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I've been dragging this question for a while now, but..

When you make an overlay plot of data + estimation, what is the appropriate bin-width?

the two extremes correspond to:

single-bin for the whole variable range --> so you get the overall normalizations

too many bins in the variable range --> so you don't get anything but a flat or even broken (here and there) distribution, with 0 or 1's for the data.

My question is then, is there a rule of thumb one can use to decide the bin numbers? (eg to have reasonable errors) This becomes a little more complicated for variable-binning. For example, I was recommended that in the region where my data is rare, i should go with larger binwidths, but I don't understand the reason why. And I've seen plots that people don't do that [eg the plot here where they show the mT-distribution for a W' search... it looks like it has a fixed binwidth, but the MC-Data ratio seems to have a weirdly varied one].

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# A Binning of a variable

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