Question

The distribution of age for players of a certain professional sport is strongly skewed to the right with mean 26.8 years and standard deviation 4.2 years. consider a random sample of 4 players and a different random sample of 50 players from the population. What would be true about the sampling distributions of the sample mean ages for samples of size 4 and samples of size 50?

Answer 1

For a random sample of 4 players, the sampling distribution of the sample mean age would have:

the mean wouldn't be close to 26.8 years, as a sample mean will be an unbiased estimator of the population mean but the sample here is far smaller than a sample of 50.

A larger standard deviation compared to the standard deviation of individual player ages (4.2 years), as the standard deviation of the sample mean decreases with larger sample sizes (i.e., the Central Limit Theorem). The standard deviation of the sample mean for a sample of 4 players can be calculated using the formula:

s_mean = s / sqrt(n)

where s is the population standard deviation (4.2 years) and n is the sample size (4).

For a random sample of 50 players, the sampling distribution of the sample mean age would have:

A mean close to 26.8 years, as the sample mean will be an unbiased sample mean for a sample of 4 players, as the standard deviation of the estimator of the population mean.

A smaller standard deviation compared to the standard deviation of the sample mean decreases with larger sample sizes (i.e., the Central Limit Theorem). The standard deviation of the sample mean for a sample of 50 players can be calculated using the formula:

s_mean = s / sqrt(n)

where s is the population standard deviation (4.2 years) and n is the sample size (50).