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 used for estimated the uncertainty, and the resulting error computed by ROOT is the square root of the sum of the square of the weights. In older ROOT version you needed to call Sumw2(true) to get this error.
If your histogram is the result of something else (e.g. division of two other histograms) then a correct error estimate is more complicated and probably you would need to use a specialised method such as bootstrapping or Monte Carlo simulation.
Lorenzo