The Central Limit Theorem states that when you take a large number of random samples from a population, the distribution of the sample means will tend to be normally distributed, regardless of the population's original distribution. This means that even if the data is skewed or not normally distributed, the averages of those samples will form a bell-shaped curve as the sample size increases. This theorem is crucial in statistics because it allows researchers to make inferences about a population using sample data. It underpins many statistical methods, including hypothesis testing and confidence intervals, making it a foundational concept in the field of statistics.