Converge.

Three chaoses, one bell. The Central Limit Theorem, poured out live — a flat, a skew, and a split, all finding the same curve.
Sample size
Set sample size to 1 — see the chaos
On the far side of the noise

A single draw is a wild card. The average of a handful of them is a tamed one — and average enough handfuls, from any source at all, and the same bell appears.

01 Three ugly sources

The left lane draws from a flat uniform, the middle from a hard-skewed exponential, the right from a split bimodal. Nothing about these shapes is bell-like. Set the sample size to one and each histogram simply wears its source's face.

02 Take the mean

Instead of plotting single draws, each lane draws a small sample of n, keeps only its mean, and pours that one number into the histogram. Raise n and the skew and the split both dissolve — the averages forget where they came from.

03 The same curve

The amber overlay is identical in all three lanes: a Normal with the same centre and a width that shrinks like 1/√n. Chance, piled deep enough, stops being chaotic and becomes iron law. That is the Central Limit Theorem.