CLT, or Central Limit Theorem, is a fundamental principle in statistics that states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the original population's distribution. This means that if you take many random samples from a population and calculate their means, those means will form a bell-shaped curve, known as a normal distribution, as the number of samples grows. The Central Limit Theorem is crucial for making inferences about populations based on sample data. It allows statisticians to use normal distribution properties to estimate probabilities and conduct hypothesis testing, even when the underlying population is not normally distributed.