16 a 1994 article in science magazine discussed a

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16. A 1994 article in Science magazine discussed a study comparing the health of 6000 vegetarians and a similar number of people who were not vegetarians. The vegetarians had a 28% lower death rate from heart attacks. (a) Is this an observational study or an experiment? _____________________________________ (b) Give an example of a potential confounding variable and explain what it means to say that this is a confounding variable. (c) Give an example of an extraneous variable that you would not expect to be a confounding variable. Explain why you think this variable would not be confounding. Homework: pg 380-3 problems 5.61-3, 66, 68, 70-72
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Chapter 5: Producing Data From the AP Stats Listserv a response from one of the “fathers” of AP Statistics: Chris and Peter, I have always liked the way George Cobb presents this idea in experimental design. In Introduction to the Design and Analysis of Experiments, George Cobb (1998) describes the variability inherent an experiment in the following way: Any experiment is likely to involve three kinds of variability: (1) Planned, systematic variability. This is the kind we want since it includes the differences due to the treatments. (2) Chance-like variability. This is the kind our probability models allow us to live with. We can estimate the size of this variability if we plan our experiment correctly. (3) Unplanned, systematic variability. This kind threatens disaster! We deal with this variability in two ways, by randomization and by blocking. Randomization turns unplanned, systematic variation into planned, chance-like variation, while blocking turns unplanned, systematic variation into planned, systematic variation. The management of these three sources of variation is the essence of experimental design. The management of the unplanned, systematic variability (3) is the issue between you and Peter. One important goal of randomization is to turn this systematic variability into chance-like variation that adds into our standard error. If that randomization "fails", then all bets are off. Randomization is never a guarantee against confounding variables, but it is our best defense. This is an example I created to try to clarify the situation. (Linda Young bears no responsibility in what follows, but, nevertheless, it is all her fault.) To focus the discussion, consider the variation inherent in the following experimental setting. To keep the computations simple and clear, the sample size is unrealistically small. I hope the gain in simplicity and clarity from this example outweighs the obvious problem with sample size. Example Experiment: Compare two kinds of rabbit food on weight gain (in ounces) from the age of two weeks to the age of six weeks of life. We want to know if the rabbits will gain more weight on one diet than on the other. We have space to house eight rabbits for this experiment.
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