The Central Limit Theorem states that when you take a large number of random samples from any population, the distribution of the sample means will tend to form a normal distribution, regardless of the original population's shape. This means that even if the original data is skewed or irregular, the averages of those samples will cluster around the true population mean. This theorem is crucial in statistics because it allows us to make inferences about a population using sample data. For example, if researchers want to understand the average height of people in a city, they can take several random samples, calculate their averages, and rely on the Central Limit Theorem to assume that these averages will follow a normal distribution.