1. Copyright © 2014 Pearson Education. All rights reserved. 1.2-1 1.2 Sampling LEARNING GOAL Understand the importance of choosing a representative sample and become familiar with several common methods of sampling.

2. Copyright © 2014 Pearson Education. All rights reserved. 1.2-2 Slide 1.2- 2 Definition A _____________is the collection of data from every member of a population. Definition A _________________is a sample in which the relevant characteristics of the sample members are generally the same as the characteristics of the population.

3. Copyright © 2014 Pearson Education. All rights reserved. 1.2-3 Slide 1.2- 3 Definition of Bias A statistical study suffers from ____________if its design or conduct tends to favor certain results.

4. Copyright © 2014 Pearson Education. All rights reserved. 1.2-4 SAMPLING METHODS Slide 1.1- 4

5. Slide 1.2- 5 Simple Random Sampling: Every sample of the same size has an equal chance of being selected. Computers are often used to generate random numbers.

6. Slide 1.2- 6 Convenience Sampling : We use a sample that happens to be convenient to select. Use results that are readily available.

7. Slide 1.2- 7 Systematic Sampling : We use a simple system to choose the sample, such as selecting every 10th or every 50th member of the population. Select every kth member.

8. Slide 1.2- 8 Cluster Sampling : Divide the population into clusters, randomly select some of those clusters, then choose all members of the selected clusters.

9. Slide 1.2- 9 Stratified Sampling : Partition the population into at least two strata, then draw a sample from each.

10. Copyright © 2014 Pearson Education. All rights reserved. 1.2-10 METHODS OF SAMPLING Slide 1.1- 10

11. Copyright © 2014 Pearson Education. All rights reserved. 1.2-11 Slide 1.2- 11 EXAMPLE 1 A Representative Sample for Heights Suppose you want to determine the mean height of all students at your school. Which is more likely to be a representative sample for this study: the men’s basketball team or the students in your statistics class?

12. Copyright © 2014 Pearson Education. All rights reserved. 1.2-12 Slide 1.2- 12 EXAMPLE 2 Why Use Nielsen? Nielsen Media Research earns money by charging television stations and networks for its services. For example, NBC pays Nielsen to provide ratings for its television shows. Why doesn’t NBC simply do its own ratings, instead of paying a company like Nielsen to do them?

13. Copyright © 2014 Pearson Education. All rights reserved. 1.2-13 Slide 1.2- 13 EXAMPLE 3 Telephone Book Sampling You want to conduct an opinion poll in which the population as all the residents in a town. Could you choose a simple random sample by selecting names from the local telephone book?

14. Slide 1.2- 14 EXAMPLE 4 When Systematic Sampling Fails You are conducting a survey of students in a co-ed dormitory in which males are assigned to odd-numbered rooms and females are assigned to even-numbered rooms. Can you obtain a representative sample when you choose every 10 th room?

15. Slide 1.2- 15 EXAMPLE 5 Salsa Taste Test A supermarket wants to decide whether to carry a new brand of salsa, so it offers free tastes at a stand in the store and asks people what they think. What type of sampling is being used? Is the sample likely to be representative of the population of all shoppers?

16. Slide 1.2- 16 EXAMPLE 6 Gasoline Prices You want to know the mean price of gasoline at gas stations located within a mile of rental car locations at airports. Explain who you might use cluster sampling in this case.

17. Slide 1.2- 17 EXAMPLE 7 Unemployment Data The U.S. Labor Department surveys 60,000 households each month to copile its unemployment reprot. To select these households, the Department first groups cities and counties into about 2000 geographic areas. It then ramdomly selects households to survey within these geographic areas. How is this an example of stratified sampling? What are the strata? Why is stratified sampling important in this case?

18. Slide 1.2- 18 Sampling Methods Keep in mind the following three key ideas: A study can be successful only if the sample is representative of the population. A biased sample is unlikely to be a representative sample. Even a well-chosen sample may still turn out to be unrepresentative just because of bad luck in the actual drawing of the sample.