Hi, I have implemented some approximations for studentized range quantiles and probabilities based on John R. Gleason's (1999) "An accurate, non-iterative approximation for studentized range quantiles." Computational Statistics & Data Analysis, (31), 147-158. Probability approximations rely on scipy.optimize.fminbound. The functions accept both scalars or array-like data thanks to numpy.vectorize. A fair amount of validation and testing has been conducted on the code. More details can be found here: http://code.google.com/p/qsturng-py/ I welcome any thoughts as to whether you all think this might be useful to add to SciPy or make into a scikit. Any general comments would be helpful as well. I should mention I'm a cognitive neuroscientist by trade, my use of statistical jargon probably isn't that good. Regards, Roger Roger Lew