The law of large numbers is a theorem in probability theory stating that as the number of independent, identically distributed trials increases, the sample average of their outcomes converges to the expected value of the underlying distribution. It provides the theoretical justification for estimating expectations by averaging repeated random samples, as performed in Monte Carlo methods and Monte Carlo integration. Weak and strong forms of the theorem differ in the mode of convergence they guarantee.