Chapter 12. Sample Surveys Producing Valid Data “If you don’t believe in random sampling, the next time you have a blood test tell the doctor to take it all.”. The election of 1948 The Predictions The Candidates Crossley Gallup Roper The Results Truman 45 44 38 50
Producing Valid Data
“If you don’t believe in random sampling, the next time you have a blood test tell the doctor to take it all.”
The Candidates Crossley Gallup Roper The Results
Truman 45 44 38 50
Dewey 50 50 53 45
1. Think about sampling something you are cooking—you taste (examine) a small part of what you’re cooking to get an idea about the dish as a whole.
2. Opinion polls are examples of sample surveys, designed to ask questions of a small group of people in the hope of learning something about the entire population.
Convenience sampling: Just ask whoever is around.
Ann Landers summarizing responses of readers
70% of (10,000) parents wrote in to say that having kids was not worth it—if they had to do it over again, they wouldn’t.
Bias: Most letters to newspapers are written by disgruntled people. A random sample showed that 91% of parents WOULD have kids again.
Bias: People have to care enough about an issue to bother replying. This sample is probably a combination of people who hate “wasting the taxpayers money” and “animal lovers.”
Administrators at a hospital are concerned about the possibility of drug abuse by people who work there. They decide to check on the extent of the problem by having a random sample of the employees undergo a drug test. The administrators randomly select a department (say, radiology) and test all the people who work in that department – doctors, nurses, technicians, clerks, custodians, etc.
Name the kind of bias that might be present if the administration decides that instead of subjecting people to random testing they’ll just…
Individuals are randomly selected. No one group should be over-represented.
Sampling randomly gets rid of bias.
Random samples rely on the absolute objectivity of random numbers. There are tables and books of random digits available for random sampling.
Statistical software cangenerate random digits (e.g., Excel “=random()”,
ran# button on
04905 83852 29350 91397 19994 65142 05087 11232Hospital example (cont.)
Example: All humans, all working-age people in California, all crickets
A parameter is a number describing a characteristic of the population.
Sample: The part of the population we actually examine and for which we do have data.
How well the sample represents the population depends on the sample design.
A statistic is a number describing a characteristic of a sample.Population versus sample
00 Abbott 07 Goodwin 14 Pillotte 21 Theobald
01 Cicirelli 08 Haglund 15 Raman 22 Vader
02 Crane 09 Johnson 16 Reimann 23 Wang
03 Dunsmore 10 Keegan 17 Rodriguez 24 Wieczoreck
04 Engle 11 Lechtenb’g 18 Rowe 25 Williams
05 Fitzpat’k 12 Martinez 19 Sommers 26 Wilson
06 Garcia 13 Nguyen 20 Stone 27 Zink
76509 47069 86378 41797 11910 49672 88575
Rodriguez (17) Lechtenberg (11) Engle (04)
19689 90332 04315 21358 97248 11188 39062
19 Summers, 03 Dunsmore, 04 Engle
1 under $15,000 25% 250
2 15,000-29,999 40% 400
3 30.000-50,000 30% 300
4 over $50,000 5% 50Stratified Random Sampling
A sample of size 1,000 is to be drawn
develop a complete list of the
population members (making
it difficult to develop a simple
random sampling procedure.)
e.g., all items sold in a grocery store
the population members are widely
e.g., all Toyota dealerships in North
reading level of our course text
based on the length of the sentences.
Strata are homogenous (e.g., male, female) but
differ from one another
Most surveys conducted by professional polling organizations and government agencies use some combination of stratified and cluster sampling as well as simple random sampling.