1. The Language of Sampling Lecture 6 Sections 2.1 – 2.4 Fri, Jan 26, 2007

2. Why Sample? Studying a sample gives us only partial information about a population. So why not study (observe) the entire population? Samples are random, so how can we expect a sample to be representative of the population?

3. Why Sample? We can prove mathematically that the larger a sample is, the more likely it is to be representative of the population. More specifically, the “margin of error” for large samples is very small.

4. The Language of Sampling Unit or subject. Variable. Population size N. Sample size n. Parameter. Statistic.

5. Parameters and Statistics For numerical data, we usually use the average of the values in the sample. For non-numerical data, we usually use the proportion of observations in a specific category.

6. Random vs. Representative Random sample. Representative sample.

7. Example Study: Men Enjoy Watching Bad Guys Suffer Study: Men Enjoy Watching Bad Guys Suffer  What were the populations?  What were the samples?  What were the variables?  What statistics were used?  What were the parameters?

8. Bias A sampling method is biased if it systematically produces a sample whose characteristics differ from those of the population, i.e., unrepresentative. Note that it is the method that is biased, not the sample.

9. Biased Sampling Two characteristics that biased sampling methods often exhibit.  Convenience sample.  Volunteer sample.

10. Four Types of Bias Selection bias. Nonresponse bias. Response bias. Experimenter bias (Sec. 3.5, p. 176).

11. Whose Fault is it? Selection bias originates with the sampling procedure. Nonresponse bias originates with the subjects who were selected for the sample, but chose not to participate. Response bias originates with the subjects who are in the sample.

12. Whose Fault is it? Experimenter bias originates with the experimenter.

13. Examples Phone surveys. Use random-digit dialing.  Convenience sample?  Volunteering sample?  Selection bias?  Non-response bias?  Response bias?  Experimenter bias?

14. Examples Mailed surveys, including e-mail. Mail individuals a survey and ask them to respond.  Convenience sample?  Volunteering sample?  Selection bias?  Non-response bias?  Response bias?  Experimenter bias?

15. Examples Internet survey. Post the survey questions on the internet and let visitors respond at will.  Convenience sample?  Volunteering sample?  Selection bias?  Non-response bias?  Response bias?  Experimenter bias?

16. Examples Estimating average family size. Randomly select individuals and ask them how many siblings they have.  Convenience sample?  Volunteering sample?  Selection bias?  Non-response bias?  Response bias?  Experimenter bias?

17. Monday Bring your TI-83 or TI-84 !