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# Sampling and Bias

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A noted scientist Dr. Iconu wanted to investigate attitudes toward television advertising among American college students. He decided to use a sample of 100 students. Students in freshman psychology (PSY 001) are required to serve as subjects for experimental work. Dr. Iconu obtained a class list for PSY 001 and chose a simple random sample of 100 of the 340 students on the list. He asked each of the 100 students in the sample the following question:

Do you agree or disagree that having commercials on TV is a fair price to pay for being able to watch it?

Of the 100 students in the sample, 82 marked "Agree." Dr. Iconu announced the result of his investigation by saying, "82% of American College Students are in favor of TV commercials."

(a) What is the population in this example?
(b) What is the sampling frame in this example?
(c) Explain briefly why the sampling frame is or is not suitable.
(d) Discuss briefly the question Dr. Iconu asked. Is it a slanted question?
(e) Discuss briefly why Dr. Iconu's announced result is misleading.
(f) Dr. Iconu defended himself against criticism by pointing out that he had carefully selected a simple random sample from his sampling frame. Is this defense relevant?

Submit well thought out responses to questions posed in (a), (b), (c), (d), (e), and (f) in a message to your class Discussion Board. Remember, you only need to respond to two other students for 1.1. There is no need to respond to others' work on the above, but it would be a good idea to read a few of them.

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#### Solution Preview

(a) What is the population in this example?

The population is all American college students.

(b) What is the sampling frame in this example?

The sample is the 100 students selected from the psychology class. The sampling frame (the list of all the sampling units in the target population) should be a roster of all American college students. Instead, it is the students in the freshman psychology class.

(c) Explain briefly why the ...

#### Solution Summary

The solution discusses sampling and bias.

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