Based on the Central Limit Theorem, the mean of the sampling distribution is equal to the mean of the population.

Given the information:

• Population mean ($\mu$) = 24.4
• Sample size ($n$) = 37

The mean of the sampling distribution is: $\text{Mean of the sampling distribution} = \mu = 24.4$

Answer:

The mean of the sampling distribution = 24.4

Would you like to go into more details or have any questions?

Here are five related questions to expand your understanding:

1. What is the formula for the standard error of the mean in a sampling distribution?
2. How does increasing the sample size $n$ affect the standard error of the mean?
3. What happens to the shape of the sampling distribution if the sample size is less than 30?
4. Why does the Central Limit Theorem assume that the sampling distribution becomes approximately normal for $n \geq 30$?
5. How can the Central Limit Theorem be used to make inferences about the population from the sample?

Tip: The larger your sample size, the closer the sampling distribution of the mean will approximate a normal distribution, even if the population itself is not normally distributed.