Ch. 7 Quiz ODS 262

If the expected value of a sample statistic is equal to the parameter it is estimating, then we call that sample statistic

unbiased

The standard error of the mean:

All of these.

Why is the Central Limit Theorem so important to the study of sampling distributions?

It allows us to disregard the shape of the population when n is large.

Suppose a sample of n = 50 items is drawn from a population of manufactured products and the weight, X, of each item is recorded. Prior experience has shown that the weight has a probability distribution with mean = 6 ounces and standard deviation = 2.5 ounces. Which of the following is true about the sampling distribution of the sample mean if a sample of size 15 is selected?

The mean of the sampling distribution is 6 ounces.

True or False: The Central Limit Theorem is considered powerful in statistics because it works for any population distribution provided the sample size is sufficiently large and the population mean and standard deviation are known.

True

True or False: The standard error of the mean is also known as the standard deviation of the sampling distribution of the sample mean.

True

The Central Limit Theorem is important in statistics because

for a large n, it says the sampling distribution of the sample mean is approximately normal, regardless of the shape of the population.

For sample sizes greater than 30, the sampling distribution of the mean will be approximately normally distributed

regardless of the shape of the population.

Selection of raffle tickets from a large bowl is an example of

sampling without replacement.

Sampling distributions describe the distribution of

statistics