Question Type:
Weaken (Looks like Flaw, but all answers are prefaced by "fails to consider the possibility that …")
Stimulus Breakdown:
Conclusion: Disciplining dogs actually just encourages misbehavior.
Evidence: There's a correlation between disciplining dogs and misbehaving dogs.
Answer Anticipation:
We're always evaluating Correlation vs. Causality with the same two pressure points: 1. OTHER ways to explain the same correlation 2. Plausibility of Author's Causal Story As we consider other ways to explain the same correlation, our first thought should always be "reverse causality", i.e. has the author failed to consider that what she thinks is the cause and the effect are actually backwards? Maybe the correlation between disciplining and misbehaving is because the MISBEHAVING causes the disciplining, not the other way around. That's such a plausible objection in this case that I would probably feel confident just going to the answers looking for that sort of answer.
Correct Answer:
A
Answer Choice Analysis:
(A) Bingo! Reverse causality.
(B) It doesn't weaken the author if we know that dogs learn how their owners will react. It only weakens the author if dogs start to improve their behavior in order to avoid angering their owners.
(C) "Other than dogs" immediately reveals this is out of scope.
(D) This does nothing but maybe strengthen. We MIGHT have been able to weaken the argument by offering an alternate explanation for the evidence like "It’s not the disciplining itself that's causing the misbehavior, it's just the incompetent dog raising abilities of these kennel club members". Since this answer rules out that alternate explanation, it actually drifts toward strengthening.
(E) We don't really need to compare kennel club members to other dog owners. The statistic we're trying to explain is showing an asymmetry among the kennel club members, not an asymmetry between kennel club and other dog owners.
Takeaway/Pattern: The single biggest pattern in LR is to present a correlation between X and Y and then conclude that X caused Y. We want to always consider alternate explanations (maybe Y causes X, maybe it's a coincidence, maybe Z causes both X and Y) or otherwise look for answers that increase/decrease the plausibility that X causes Y.
#officialexplanation