Random numbers from a radioactive source
Here’s a fun link which talks about the difference between truly random numbers and pseudo-random numbers. When we teach this, we often mention generation of random numbers (or at least the random number seed) from a radioactive source as one way of getting truly random numbers. Here is someone actually doing it. The sequel is well worth a watch too if you have the time.
James Curran's interests are in statistical problems in forensic science. He consults with forensic agencies in New Zealand, Australia, the United Kingdom, and the United States. He produces and maintains expert systems software for the interpretation of evidence. He has experience as an expert witness in DNA and glass evidence, appearing in courts in the United States and Australia. He has very strong interests in statistical computing, and in automation projects. See all posts by James Curran »
Pseudo-random numbers are truly random because I don’t know what they’re going to be.
I’m not a fan of the word random though, I think it has the potential to create misunderstandings but not much potential for clearing up misunderstandings.
*ducks for cover*
11 years ago
I’d say whether pseudo-random numbers are truly random depends on who or what might be trying to predict them.
If you’ve got quantum or physically chaotic numbers, there are limits on how well they can be predicted no matter who is trying.
If you’re using, for example, the Mersenne Twister in R, the numbers can be predicted reasonably easily by anyone who can get a good sample of them, but they won’t accidentally correlate with anything.
In between you’ve got cryptographically strong generators that are probably unpredictable as long as they are seeded by an unpredictable source — and as long as there aren’t bugs. But predicting `random` numbers has been a source of some important attacks on computer security.
One reason I wrote the randaes package for R is that it’s occasionally useful to have a generator that you’re sure isn’t going to have any subtle patterns that are in sync with features in your simulation code.
11 years ago
That’s a great way of thinking about it.
11 years ago
Those were interesting videos, thanks for sharing.
He notes in the second video that his program squares the result then takes the square root, so as to ensure that the random number is always positive. Am I right, then, in that the most likely number (assuming that the mean he’d picked was correct) would probably be 1, not 0? The idea being that a result either 1 more or 1 fewer than the mean will turn into the number 1, whereas the number of electrons must be exactly equal to the mean in order for the random number to be 0.
Of course it’s possible that 0 is still more likely, but I imagine that’d only be the case if the distribution’s variation were very small indeed.
11 years ago