The Law of Large Numbers (LLN) is a fundamental theorem in probability and statistics that describes how the average of a large number of independent and identically distributed random variables tends to converge to the expected value as the sample size increases. Essentially, it suggests that as we conduct more trials of an experiment, the results will become more stable and representative of the true underlying distribution. This principle is crucial in various fields, including insurance, finance, and machine learning, as it underpins the reliability of predictions and statistical inferences drawn from large datasets.