The most important parameter of a histogram is the bin width, since it controls the trade-off between presenting a picture with too much detail (``undersmoothing'') or too little detail (``oversmoothing'') with respect to the true distribution. Despite this importance there has been surprisingly little research into estimation of the ``optimal'' bin width. Default bin widths in most common statistical packages are, at least for large samples, quite far from the optimal bin width. Rules proposed by, for example, Scott (1992) lead to better large sample performance of the histogram, but are not consistent themselves. In this paper we extend the bin width rules of Scott to those that achieve root-$n$ rates of convergence to the $L_2$-optimal bin width; thereby providing firm scientific justification for their use. Moreover, the proposed rules are simple, easy and fast to compute and perform well in simulations.
