Raymond Hettinger <raymond.hettin...@gmail.com> added the comment:
Several thoughts: * OVL was used often in the finance firm where I worked. * It provides a simple, easy to understand point estimate of the similarity or overlap between two PDFs. * It was far easier to use than a Students-t test to answer the question of how similar two normal distributions are and it is more precise than the more common technique of just running overlapping plots and doing it by eye. * It isn't easy for end-users to do this themselves without running an integration. * It is well defined and well motivated: See: "The overlapping coefficient as a measure of agreement between probability distributions and point estimation of the overlap of two normal densities" -- Henry F. Inman and Edwin L. Bradley Jr http://dx.doi.org/10.1080/03610928908830127 See also: https://www.rasch.org/rmt/rmt101r.htm And: http://www.iceaaonline.com/ready/wp-content/uploads/2014/06/MM-9-Presentation-Meet-the-Overlapping-Coefficient-A-Measure-for-Elevator-Speeches.pdf Perhaps, the wording can be improved on the male/female height example. Measured to finite precision, perhaps to the nearest centimeter, there will be overlaps. This is same kind of binning done with chi-square tests to compare how well two distributions match. AFAICT, this tool is well-defined, tested, and has legitimate use cases that are easy to achieve in other ways using only standard library tools. ---------- _______________________________________ Python tracker <rep...@bugs.python.org> <https://bugs.python.org/issue37905> _______________________________________ _______________________________________________ Python-bugs-list mailing list Unsubscribe: https://mail.python.org/mailman/options/python-bugs-list/archive%40mail-archive.com
