Final answer:
The Central Limit Theorem states that the distribution of sample means will be approximately normal if the sample size is large enough. The theorem also establishes the relationship between the mean of the sample means and the population mean, as well as the standard deviation of the sample means.
Step-by-step explanation:
The Central Limit Theorem (CLT) states that if a sufficiently large sample size (n) is drawn from a population, the distribution of the sample means will be approximately normal, even if the population distribution is not normal.
The mean of the sample means will be equal to the population mean, and the standard deviation of the sample means, known as the standard error of the mean, can be calculated by dividing the population standard deviation by the square root of the sample size (n).
This theorem is applicable when the sample size is large enough, and it allows us to make inferences about a population based on the distribution of sample means.