How random is random when using different RNG mobile implementations?

Just a foreword: I'm not exactly clear on how a RNG actually works.

If I write a simple routine to randomly pick a number between 0 and 1, and run this n number of times, I would expect a certain, re开发者_如何学Pythonlatively random distribution of numbers that should approach 50/50, give or take with some variance - i.e. a delta of x percent skewed one way or the other.

By looking at this variance, am I going to be able to see any meaningful patterns across a population of different devices?

For example, if I have a large population of iPhones running this routine simultaneously, would they all see a similar variance compared to running them on different days or compared to running a large batch of Android or WP7 devices? Or will the variance truly be random and be all over the place regardless of device or time or any other factor that would affect the randomness of the distribution?

This completely depends on several important factors in the PRNG. A PRNG (Pseudo-Random-Number-Geneator) is an algorithm, which can only simulate random numbers, and is initialized with an input state.

PRNGs are measured with their periodicity (how soon until it loops around), their distribution, and how easy is it to derive the next value from previous values or a known seed. All of these properties are very important to PRNGs used for cryptographic purposes.

So in short, it completely varies upon the algorithm in use by any of those devices. Provided the input state and algorithm are the same, the output can be expected to be the same.

If you want to test the quality of a PRNG, you can use the guidelines in FIPS-140-2, or use the DieHarder test suite.

Also refer to the Wikipedia page.