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The total probability formula, \(P(A) = \sum_k P(A \cap B_k)\), relates the occurrence of an event \(A\) to that of several mutually exclusive events (\(B_k\)'s).

The binomial theorem, making use of the well-known binomial coefficients, allows expressing \((x+y)^n\) as a sum: \((x+y)^n=\sum_{k=0}^{n} \binom{n}{k} x^k y^{n-k}\).

The normal density, \(p(x) = \frac{1}{\sigma \sqrt{2\pi}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\), has become important in many applications because of its close relationship to the central limit theorem.