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 number of trials increases. Essentially, it states that as more observations are collected, the sample mean will get closer to the population mean. This principle is crucial in various fields, including finance, insurance, and artificial intelligence, as it underpins the reliability of predictions and outcomes based on large datasets. Understanding LLN helps in making informed decisions based on statistical data.