Its definition, its many applications, its deep connection with inverse probability, and a glimpse at its history — For all its heft, the Central Limit Theorem has a singularly succinct definition. It says, simply, the following: the standardized sum or mean of a sample of i.i.d. random variables converges in distribution to N(0,1). Built around this central idea is a modest-sized lattice of variations and special cases. But…