The Central Limit Theorem describes a fundamental principle: the distribution of sample means, taken from almost any population, will tend towards a normal distribution as the sample size grows sufficiently large. This remarkable phenomenon occurs regardless of the original population's shape, making it a cornerstone for statistical inference and hypothesis testing.