Final answer:
The Central Limit Theorem states that the distribution of sample means will be normal if the sample size is large enough, with the mean of this distribution being equal to the population mean.
Step-by-step explanation:
The Central Limit Theorem answers the question of what happens to the distribution of sample means, when samples of sufficient size are drawn from a population. Specifically, it states that this distribution will tend toward a normal distribution, regardless of the population's original distribution. The mean of the sampling distribution of (x-bar) is equal to the population mean.
Therefore, if we have a random variable X with mean μ, then the mean of the sampling distribution of the sample means, or μₓ, will also be μ. This holds true irrespective of the original distribution of X, whether it is normal or not.