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The Weak Law of Large Numbers (WLLN) says that the sample average of independent, identically distributed (i.i.d.) random variables with finite mean gets close to the true mean with high probability as the sample size grows.
Monte Carlo estimation approximates an expected value by averaging function values at random samples drawn from a probability distribution.