The Central Limit Theorem states that, when independent random variables are added, their normalized sum tends to follow a normal distribution, regardless of the original distributions of the variables, as the number of variables increases. This theorem is crucial because it allows us to make inferences about population means based on sample means, reinforcing the importance of independence in random variables for accurate statistical analysis.