Final answer:
The mean of a normal population is specific to the population being considered and is not one of the provided options. In a normally-distributed population with a known mean and standard deviation, the sampling distribution of the sample mean will also be normally distributed according to the Central Limit Theorem.
Step-by-step explanation:
The mean of a normal population cannot be directly identified with the options provided since it is completely determined by the context of the population being analyzed. In the context of a normally-distributed population with a known mean, such as the population mentioned in the question with a mean of 50 and a standard deviation of four, the sampling distribution of the sample mean can be described when 100 samples of size 40 are drawn from this population.
According to the Central Limit Theorem, the sampling distribution of the sample mean will be normally distributed, even if the population distribution is not normal. For large enough sample sizes (typically n > 30), the sample means will approximate a normal distribution, regardless of the shape of the population distribution. In our specific scenario, with 100 samples of size 40 from a population with a mean of 50 and a standard deviation of four, the expected mean of the sampling distribution will be equal to the population mean (50), and the standard deviation (often called the standard error) of this sampling distribution will be the population standard deviation (four) divided by the square root of the sample size, which is four divided by the square root of 40.