Talk:Random number generation

Randomness is an observed entity
The article starts out with: "A random number generator (RNG) is a computational or physical device designed to generate a sequence of numbers or symbols that lack any pattern, i.e. appear random." But it should be the other way around: If an Observer find that a sequence lack any pattern, it appears random to him. Different observers may rate the same sequence differently. The randomness is not in the sequence.

Bo Domstedt http://www.trng98.se

== Random number generation input through analogue computer filtering as an attractor which shapes the range of the output* == write about it please

True vs. pseudo-random numbers
The wording in this section (Random number generation) is misleading. It mixes together the theoretical underpinnings: true random numbers / pseudo random numbers and practical implementations. In the theoretical sense, the true- and pseudo-random numbers are two different beasts with their own benefits and drawbacks: In practical designs the true random generator actually includes a pseudorandom one. See, for example, NIST SP 800-90A, so there is no "vs.". Also, in the physical world, any entropy source is very fragile (for the simple reason that most of its non-catastrophic failures are extremely hard - or even impossible - to detect). Dimawik (talk) 22:04, 2 March 2024 (UTC)
 * 1) True random numbers are secure in cryptographic sense against future disclosures (cf. Forward secrecy)
 * 2) The very predictability of the pseudorandom numbers is desirable for most applications (except cryptography and gaming machines, AFAIK).

Defects of simple algorithms
Thank you for adding a reference to Nishimura's work. However, I do not quite understand how this work is relevant to this section. contains some language on p. 5, but I have hard time interpreting it to support the sentence "are unsuitable where high-quality randomness is required, such as ... statistics" (ellipsis is mine). Can you help me? Dimawik (talk) 06:13, 25 June 2024 (UTC)


 * Thanks for following up on this. If you use a poor quality PRNG, some statistical methods will produce poor results (e.g. over/underestimating variance). Many algorithms are not very sensitive to this, so often it doesn't matter. However, the cost of using a good one is not very high, so the rule of thumb is to use a decent one to be safe. I haven't been able to find a good reference spelling this out though. (Maybe this? http://www0.cs.ucl.ac.uk/staff/d.jones/GoodPracticeRNG.pdf)
 * I think the cleanest solution would be to remove the "statistics" part and just keep the "cryptography" part, for which a good quality PRNG is clearly important. Aezarebski (talk) 10:56, 25 June 2024 (UTC)