On Sun, 7 Jun 2015 10:52 pm, Steven D'Aprano wrote: > The median is unchanged, the mean shifts slightly higher, and the standard > deviation increases. But as you can see, these are not particularly > powerful tests of randomness: only the mode shows an obvious deviation > from uniformity.
Oh, I forgot: we can look at the frequencies themselves as well. If our sample is absolutely perfectly distributed uniformly, then every element will have a frequency of exactly 10000. We don't necessarily expect this from a random sample, but if we're too far from that, we should be concerned. Here are the results from the fair sample: py> statistics.mean(f.values()) 10000.0 py> statistics.median(f.values()) 9995.0 py> statistics.stdev(f.values()) 91.8027089673141 If I do the same thing with the biased sample, the standard deviation stands out like a sore thumb: py> statistics.mean(g.values()) 10000.0 py> statistics.median(g.values()) 9993.0 py> statistics.stdev(g.values()) 1442.527341617181 -- Steven -- https://mail.python.org/mailman/listinfo/python-list
