First off, this question is ripped out from this question. I did it because I think this part is bigger than a sub-part of a longer question. If it offends, please pardon me.
Assume that you have a algorithm that generates randomness. Now how do you test it? Or to be more direct – Assume you have an algorithm that shuffles a deck of cards, how do you test that it’s a perfectly random algorithm?
To add some theory to the problem – A deck of cards can be shuffled in 52! (52 factorial) different ways. Take a deck of cards, shuffle it by hand and write down the order of all cards. What is the probability that you would have gotten exactly that shuffle? Answer: 1 / 52!.
What is the chance that you, after shuffling, will get A, K, Q, J … of each suit in a sequence? Answer 1 / 52!
So, just shuffling once and looking at the result will give you absolutely no information about your shuffling algorithms randomness. Twice and you have more information, Three even more…
How would you black box test a shuffling algorithm for randomness?
Statistics. The de facto standard for testing RNGs is the Diehard suite (originally available at http://stat.fsu.edu/pub/diehard). Alternatively, the Ent program provides tests that are simpler to interpret but less comprehensive.
As for shuffling algorithms, use a well-known algorithm such as Fisher-Yates (a.k.a ‘Knuth Shuffle’). The shuffle will be uniformly random so long as the underlying RNG is uniformly random. If you are using Java, this algorithm is available in the standard library (see Collections.shuffle).
It probably doesn’t matter for most applications, but be aware that most RNGs do not provide sufficient degrees of freedom to produce every possible permutation of a 52-card deck (explained here).