I try to make a histogram and then fit some distribution to it by means of chi2. The Knuth rule (I have some bimodal cases so I'm not using Freedman-Diaconis or Scott) gives me the following histogram (there are 914 data points):
The problem is that there are two bins with less than 5 counts, so it's not really justified to believe a result of a chi2 fitting. How can I cope with this problem? Should I just merge the underpopulated bins with their neighbours (variable bin-widths)? I tried manually playing with the number of (equal-width) bins checking if there's a binning with all the counts $\geq 5$ but with no luck. What about just excluding the data from the underpopulated bins (is it justified to call them outliers?)?