Final answer:
The Central Limit Theorem describes how sample means and sums tend to follow a normal distribution as the sample size increases, even if the original population is not normally distributed, particularly for samples generally larger than 30.
Step-by-step explanation:
The Central Limit Theorem (CLT) is a fundamental principle in statistics that applies to sample means and sums. It states that if samples of sufficient size are drawn from a population, the sampling distribution of the sample means tends to be normal, regardless of the population's distribution. When it comes to sums, the CLT also asserts that as sample size increases, the distribution of the sums approaches a normal distribution. Specifically, for samples with a size usually considered to be 30 or more, the CLT predicts that the sampling distribution of the sample mean will be approximately normal with a mean equal to the population mean and a standard deviation equal to the population standard deviation divided by the square root of the sample size.