Final answer:
The scenario illustrates the Central Limit Theorem for Sample Means, which states that the distribution of sample means will be approximately normal if the sample size is sufficiently large.
Step-by-step explanation:
The scenario described illustrates the concept of the Central Limit Theorem for Sample Means. According to this theorem, if the sample size is sufficiently large, the distribution of the sample means will be approximately normal, regardless of the shape of the population distribution. The mean of the sample means will be equal to the population mean, and the standard deviation of the sample means, called the standard error of the mean, will be equal to the population standard deviation divided by the square root of the sample size.