TLDR; I think this could be a useful contribution to NumPy, but I want to get feedback on where it should go (either in NumPy or elsewhere). I have functions using numpy.random which invoke the 8 "Real" data sets as estimated by Ted Micceri in 1989. These can be useful in Monte Carlo simulations.
Background info: Parametric inferential statistics generally assume normal distributions (though kurtosis presents less of an issue than skew). However, in "nature", distributions are often not normal. In 1989, Ted Micceri's study ( http://psycnet.apa.org/record/1989-14214-001) on real data sets resulted in the estimation of 8 "Real" distributions. Using these distributions in simulations help to produce more realistic types I and II error rate and power estimates, particularly for smaller samples. A similar module is currently available in Fortran called realpops.
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