The central limit theorem: if random variables $\{X_n\}_{n \in \mathbb{N}}$ are (A) Independent, (B) Identically distributed, and (C) have finite variance then (D) $(\sum_1^n X_i - n\mu)/\sqrt{\sigma^2 n} \to N(0,1)$.