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Unformatted text preview: 2. a) The first premise (in which S is a sample of X s) . For there to be a problem here, the sample must either be unrepresentative or biased. See pp. 235–239. 3. b) Describe the features of the sample which bias it. The sample is based on a voluntary online survey, and both features raise concerns. The poll’s being online excludes all shoppers who do not have internet access, and the poll’s being voluntary excludes all shoppers who aren’t interested in participating. Both are potential sources of sample bias. A good counter argument would identify both problems and discuss how they are problematic. See pp. 239– 240. Good Reasoning Matters! A Constructive Approach to Critical Thinking , Fifth Edition © Oxford University Press Canada, 2012 Passage 4 1. c) General causal reasoning. General causal reasoning proceeds as follows: X is correlated with Y ; the correlation between X and Y is not due to chance; the correlation between X and Y is not due to some mutual cause Z ; Y is not the cause of X ; therefore, X causes Y . See pp. 227–229, 233–235, 241–242. 2. b) The second premise (in which the correlation between X and Y is not due to chance). For there to be a problem here, it must be that the correlation is due to chance or the possibility of chance has not been ruled out. See pp. 243–244. 3. b) Show that the correlation between X and Y could be due to chance. As long as the arguer hasn’t ruled out chance—and this arguer has not—it is enough to show that the correlation could be chance in order to undermine the causal claim. A good counter argument, then, would show that this correlation could be a matter of chance, which shouldn’t be too difficult. It seems very unlikely that rain could cause flickering lights. See pp. 244–245. Passage 5 1. c) General causal reasoning. General causal reasoning proceeds as follows: X is correlated with Y ; the correlation between X and Y is not due to chance; the correlation between X and Y is not due to some mutual cause Z ; Y is not the cause of X ; therefore, X causes Y . See pp. 227–229, 233–235, 241–242. 2. b) The second premise (in which the correlation between X and Y is not due to chance). For there to be a problem here, it must be that the correlation is due to chance or the possibility of chance has not been ruled out. Even though the arguer states that the correlation is not due to chance, it is just a bald statement, without defense. See pp. 243–244. 3. b) Show that the correlation between X and Y could be due to chance. As long as the arguer hasn’t ruled out chance—and this arguer has not—it is enough to show that the correlation could be chance in order to undermine the causal claim. To construct a counter argument, then, one would need to discuss how the presence of wind turbines might not lead to health effects. Examples could be drawn from areas where there are few reported problems, or alternate explanations of the reported health effects could be discussed. See pp. problems, or alternate explanations of the reported health effects could be discussed....
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 Summer '09
 JURKOWSKI
 Logic, Districts of Vienna, Fifth Edition, Oxford University Press Canada, general causal reasoning

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