Question Type:
Match the Flaw
Stimulus Breakdown:
Conclusion: Negative ads benefit their targets.
Evidence: The winners of most elections were the targets of negative ads.
Answer Anticipation:
This is a Correlation to Causality flaw. Just because there's a correlation between winning an election and being the target of negative ads, we can't conclude that "being the target of negative ads HELPED you win the election". Authors committing this flaw are failing to consider either the inherent implausibility of their hypothesis (why would NEGATIVE ads HELP a candidate?) or failing to consider other ways to explain/interpret the correlation ... (maybe there's an association between election winners and negative ads simply because whichever candidate is leading in the polls, and thus most likely to win, is most likely to receive negative ads from competing candidates trying to bring them down).
Correct Answer:
B
Answer Choice Analysis:
(A) This evidence is not a correlation. The evidence is actually causality, and the conclusion is a normative "should" statement.
(B) This works! There's a correlation that says "most actors who win awards have had harsh reviews of their work" and concludes that "harsh reviews HELPED them (win those awards)". It matches the original both in terms of being an implausible hypothesis (why would HARSH reviews HELP their career) and in terms of failing to consider another explanation for the correlation (maybe actors who win awards are very much in the public eye and have been in a lot of films, thus increasing the likelihood that they would have received a harsh review at some point).
(C) This is close. There's a correlation between passing a course and having studied. And the author goes to conclude that studying helps with academic success. This doesn't have the same concept of the original or (B), where something seemingly HURTFUL is actually HELPFUL. The hypothesis is much more plausible. Structurally, the original thought "negative ads" were the cause and "benefit" was the effect. Its correlation was "most people who have had a certain benefit had also had this supposed cause". (B) worked the same way. WIth this correlation, though, it's "most people who have had this supposed cause have had a certain benefit". That's different, and it's actually a more persuasive statistic.
(D) There's no correlation for the evidence. It just says lots of people like something.
(E) This is not a causal conclusion. "Acceptable" is only causality if we say "an acceptable thing causes one to be okay with it."
Takeaway/Pattern: It helped here to not only think in terms of correlation -> causality but also specifically how unlikely the author's hypothesis was. It also helped structurally to notice whether we were saying "most people who have the effect, have the cause" or vice versa.
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