Central limit theorem
Named by 8 essays across 3 fields — each of them below, with the objects they name alongside it.
The average settles and the wobble does not
Two theorems are usually met a page apart and sound as though one is a sharper version of the other. They are the same sums looked at through two different magnifying glasses: divide by the number of them and everything collapses to a point, divide by its square root and a shape appears.
The tail is not a bell
The limit theorem describes a window of width one over the root of n around the mean; ask instead for the chance that an average lands a fixed distance away and the answer falls exponentially, at a rate computed from the summand before any n is chosen.
When the whole histogram deviates
A rare average has a price, an exponent that grows with the number of trials. Ask instead for the chance that the whole tally of outcomes comes out wrong, and the exponent is no longer a function of one number — it is a distance between two distributions, and every rare-average rate is a shadow of it.
The error that does not care how many dimensions
A grid gets rapidly better in one dimension and hopelessly worse in twenty. Random points get better at the same slow rate whatever the dimension, which is why a method that is bad everywhere ends up being the only one that works.
Coins hidden in the roots
The polynomial that counts permutations by their descents has no product formula, and nothing in the definition of a descent is a coin toss. But every root of the polynomial is real and negative, and a polynomial like that is a product of coins in disguise: each root r is a coin landing heads with chance 1/(1 − r). The descent count of a random permutation is exactly a sum of independent coins nobody can point to — which is why it is bell-shaped, and why its coefficients obey inequalities the inversion count breaks.
How many primes a typical number has
A typical number near N has about log log N different prime factors, and the count is spread around that in a bell curve whose variance is log log N as well. Both facts are theorems. Neither is visible at any size anyone can count: up to ten million the average is right and the spread is less than half what the limit says.
An urn forgets its start only below one half
Let each draw from an urn add balls of both colours in fixed amounts, and the long run depends on a single ratio of two eigenvalues. Below one half the urn behaves like a coin, its fluctuations spread like the square root of the draws and settle into a bell. Above one half the first few draws decide most of the outcome, the spread grows faster, and the shape that results is not a bell and depends on how the urn began.
Multiplying makes the digit one common
Multiply a few random numbers together and the product starts with 1 about 30 per cent of the time and with 9 under 5 per cent — Benford's law, which no factor contains. The logarithm of a product is a sum, the central limit theorem spreads that sum across many powers of ten, and once it is spread its fractional part is uniform. The approach is geometric, at a rate fixed by a single number for each kind of factor; sums never get there, and the powers of two get there with no randomness at all.
Named alongside it
The objects these essays reach for when they reach for this one.
VarianceConvergence rateLarge deviationsLaw of large numbersNormal distributionRate functionScalingTail boundCharacteristic functionConcentration inequalityConvergenceCounting argument