A shuffle of a finite sequence of length of distinguishable elements refers to an algorithmic process which — modulo pseudo-randomness — can be modeled as a random variable uniformly distributed on the permutations .

However, most pseudo-random entropy sources provide only a pseudo-uniformly distributed realization of , leading to the necessity of finding an algorithmic transformation process if one wishes to achieve a shuffle.

In the following, I will assume that a transforming process to a family of independent and uniformly on distributed random variables is already present for any .

One naive and seemingly correct (it is not) approach is to traverse the given sequence, uniformly swapping the current entry with another one, i.e.

void falseShuffle(uint64_t *arr, size_t len) { for (size_t j = 0; j < len; j++) swap(arr, j, unif(len)); }

as an exemplary C implementation where is independent and uniformly distributed on .

Yet, even though sensible on first sight, the above defined random variable is *only* in the most trivial cases uniformly distributed and — as empirical evidence suggests, see below — horrendously non-uniformly distributed otherwise.

To prove the non-uniformity postulated above, I first present the following number-theoretic result.

**Claim.** In only three trivial cases does the factorial of a natural number divide its tetration; formally

.

**Proof.** Let be a natural number larger than two. By the definition of the factorial, is evident. Assume . Adhering to the uniqueness of prime factorizations, follows. Observe that has to be prime since , implying which cannot hold for . **QED**

Now suppose, was indeed non-trivially distributed uniformly. Without loss of generality, all involved probability spaces were finite. Then there had to exist a surjection from this algorithm’s entropic state to with fibers of the same finite cardinality, implying . By the above proven claim, followed, making the distribution trivial. **QED**

One possible reason for the surprising nature of this non-uniformity is the striking source code resemblance to a correct implementation, i.e.

void shuffle(uint64_t *arr, size_t len) { for (size_t j = 0; j < len; j++) swap(arr, j, j + unif(len - j)); }

as an exemplary C implementation which can be inductively shown to resemble the same structure as , in each step sprinkling in some uniform randomness and thus being itself uniformly distributed.

To see just how non-uniform is, I have calculated its discrete density for :

[ | ] [ | ||| ] [ | ||| ] [ | ||| ] [ || ||||||| || ] [||||| |||||||| ||| ||] [|||||||||||||||||| || ||] [||||||||||||||||||||||||] [||||||||||||||||||||||||] [||||||||||||||||||||||||] [||||||||||||||||||||||||] [||||||||||||||||||||||||] [||||||||||||||||||||||||] [||||||||||||||||||||||||] [||||||||||||||||||||||||] n = 4

If it was uniformly distributed, the discrete density would look like a rectangle; `[||||| ... |||||]`

. Further plots for are shown in nonUniformity.txt.

Source code for the analysis and plotting: nonUniformity.hs. Empirical evidence of non-uniformity: nonUniformity.c.