Hello, I have been reading that there may be potential issues with the Box-Muller transform, which is used by the numpy.random.normal() function. Supposedly, since f*x1 and f*x2 are not independent variables, then the individual elements (corresponding to f*x1 and f*x2 ) of the distribution also won't be independent. For example, see "Stochastic Simulation" by Ripley, pages 54-59, where the random values end up distributed on a spiral. Note that they mention that they only looked at "congruential generators." Is the random number generator used by numpy congruential? I have tried to generate plots that demonstrate this problem, but have come up short. For example: import numpy , pylab nsamples = 10**6 n = numpy.random.normal( 0.0 , 1.0 , nsamples ) pylab.scatter( n[0:-1:2] , n[1:-1:2] , 0.1 ) pylab.show() I can zoom in and out, and the scatter still looks random (white noise -- almost like tv static). Does this prove that there is no problem? And if so, why does numpy do a better job than as demonstrated by Ripley? Regards, Mike Gilbert