| These are mostly about PRNG, not true random sources. brief funny story. My dad built the 5th computer in the UK, ICCE [0] and one of the tests they wanted to run was statistical analysis over random number fields. They approached the General Post Office (GPO) which had an RNG called "ernie" [1] which ran the postal investment bond lottery. This is a true RNG, based on radio device avalanche diode behaviour (actually, neon tubes). It was dressed up as a computer but it was basically a detector device and A-to-D converter dressed up to look like one. They asked for a million truly random numbers to run some tests over. Interestingly, ERNIE was made by somebody who worked on Colossus at Bletchley. The GPO refused to share a feed of numbers: They were concerned the team would discover some predictable event in the number field, and either destroy the post office bond scheme by revealing it, or use it to make millions. [0] https://en.wikipedia.org/wiki/Imperial_College_Computing_Eng... [1] https://en.wikipedia.org/wiki/Premium_Bond#ERNIE |
Going off on a tangent: An oft-neglected issue is that, even when the random source (like avalanched diodes) is actually sufficiently random, any apparatus that captures that randomness for use inherently causes a bias in the observations.
Even if everything else is perfect (it usually isn't), in terms of signal processing, any observation window (e.g. a finite length of time of measurement) is an aperture which ends up getting convolved with the signal source being observed.
It sometimes helps to convert the skew into white noise with a "whitening" post-pass algorithm.
Using real life randomness is still a good thing to do, of course, it's just that are always real world issues with anything and everything.