Week_2_Hw_Problems

Chp. 8 21. Sampling error is the difference between a sample statistic and its corresponding population parameter. A sample mean is not going to match up with the population means and the sample standard deviation will not match up exactly with the population standard deviation so the difference between the two is the sampling error. If the sample error was one is would mean that the sample very closely matched up with the population and was very reliable. 22. The reasons for sampling are: (1) sampling the entire population would be time consuming. If one were seeing how a candidate for office in a particular county was doing among women registered to vote, it would take a very long time to survey every women in that age range so a sample would make much more sense. (2) the cost of studying all items in a population may be prohibitive. When testing a product it costs a lot of money to send out samples and tabulate all the responses, the cost of doing this for an entire population may be too high. (3) the physical impossibility of checking all items in a population. Checking all of the fish in Lake Michigan would be impossible because so many fish are dying or being born on a regular basis. (4) some tests are destructive. When winery’s are testing their product they cannot test the entire crop because there would be none left to sell. (5) sample results are adequate. When the sample is done properly the sample is very representative of the population and spending extra time and money would not yield significantly different results.