In statistics, the Freedman–Diaconis rule can be used to select the size of the bins to be used in a histogram. It is named after David A. Freedman and Persi Diaconis.
For a set empirical measurements sampled from some probability distribution, the Freedman-Diaconis rule is designed to minimize the difference between the area under the empirical probability distribution and the area under the theoretical probability distribution.
The general equation for the rule is:
where
Other approaches
Another approach is to use Sturges' rule: use a bin so large that there are about