1. The mean of the sample means will be:
   same as the population mean.
   This is based on the Central Limit Theorem, which states that the distribution of the sample means will be centered around the population mean, regardless of the shape of the population distribution.

2. It is the ratio of the population standard deviation and the square root of the sample size:
   standard error of the mean.
   The standard error of the mean (SEM) is defined as the population standard deviation divided by the square root of the sample size. This provides an estimate of how much the sample mean will vary from the population mean.

Would you like further details or clarifications on either of these concepts?

Here are five related questions to explore:

1. What does the Central Limit Theorem say about sample means?
2. How does sample size affect the standard error of the mean?
3. What is the difference between population standard deviation and sample standard deviation?
4. How do we use the standard error of the mean to estimate confidence intervals?
5. Can we apply the Central Limit Theorem to non-normal distributions?

Tip: As sample size increases, the standard error of the mean decreases, making the sample mean more reliable as an estimate of the population mean.