Final answer:
According to the Central Limit Theorem, if we begin with a normally distributed population, the population mean and the mean of the sampling distribution will be the same. The standard deviation of the sampling distribution is equal to the population standard deviation divided by the square root of the sample size. The larger the sample size, the closer the sampling distribution of the means becomes normal.
Step-by-step explanation:
The Central Limit Theorem states that if samples of sufficient size are drawn from a population, the distribution of sample means will be normal, even if the distribution of the population is not normal. If we begin with a normally distributed population, the population mean and the mean of the sampling distribution will be the same. The standard deviation of the sampling distribution, called the standard error of the mean, is equal to the population standard deviation divided by the square root of the sample size. The larger the sample size, the closer the sampling distribution of the means becomes normal.