Sturge’s rule works best for continuous data that is normally distributed and symmetrical.

The Freedman-Diaconis rule is very robust and works well in practice. The bin-width is set to h=2×IQR×n−1/3h=2×IQR×n−1/3h=2\times\text{IQR}\times n^{-1/3}. So the number of bins is (max−min)/h(max−min)/h(\max-\min)/h, where nnn is the number of observations, max is the maximum value and min is the minimum value.