Intro to Algorithms
The law of large numbers is a fundamental theorem in probability theory that states as the number of trials in an experiment increases, the sample mean will converge to the expected value or population mean. This concept is crucial in understanding the reliability of averages in large samples, particularly when designing randomized algorithms where repeated trials help ensure more accurate outcomes.
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