Final answer:
The Central Limit Theorem for sample means dictates that the mean of the sampling distribution is equal to the population mean, and the standard error is the population standard deviation divided by the square root of the sample size.
Step-by-step explanation:
According to the Central Limit Theorem (CLT), for sample means, the mean of the sampling distribution of sample means (a) is equal to the population mean (μ). This holds true in a population with a known mean and standard deviation, regardless of the population's original distribution, provided the sample size is sufficiently large. The theorem also specifies that the standard deviation of the sampling distribution, known as the standard error of the mean, is calculated by dividing the population standard deviation by the square root of the sample size (n).