LR Question 6 Explanation
QUESTION TEXT: A transit company’s bus drivers are evaluated by…
QUESTION TYPE: Necessary Assumption
CONCLUSION: It is likely that the best drivers with a supervisor on-board are also the best drivers in normal conditions.
REASONING: All drivers are evaluated by supervisors and every driver is affected by their supervisor’s presence.
ANALYSIS: This is a bad argument. Many people react in different ways to the same situation. Some drivers might be very stressed by having their supervisor present. Some drivers might perform better because they want to show off. Some drivers might become nervous wrecks and crash their bus. Who knows?
The argument is assuming that all drivers react in a similar manner.
___________
- It’s only necessary that the chosen method is fair. There could be other methods.
- The argument would still be fine if the supervisors were merely competent experts.
- This would be helpful, but it’s not necessary. The argument would still be fine if this were only true of 49% of drivers (49% is not most.)
- CORRECT. Here we go. If this isn’t true and all drivers are affected differently then the evaluations won’t be fair. Some drivers will perform poorly despite the fact that they are good drivers when no supervisor is watching.
- This isn’t necessary. It’s up to the supervisors to judge the drivers’ performance.
Haha, I just realized my reasoning in the other comment was problematic since the assumption of (C) would still leave a similar problem like the one I mentioned—in this scenario, it could be that the best driver is among the 51% impacted, but the second-best driver is among the 49% not impacted, and this would also cause a problem.
The argument doesn’t depend on how many drivers are effected or how their performance changes. Instead, it depends on all drivers being affected in roughly the same way, ensuring their relative rankings stay consistent.
If the best driver’s performance drops slightly but so does the second-best driver’s, their rankings don’t change. This is what D guarantees. By focusing on the proportion or nature of the impact, C specifies conditions are that are not required for the argument to work. Keep in mind what the goal of the question is – you need to find an assumption that HAS to be present for the argument to work. C simply isn’t that.
Thanks, Aaminah, that is very clear!
But as you said, for this argument to work, we have to assume that ALL riders are affected in the same way, right? (Or at least ALL potential competitors of the best driver are affected in the same way).
That is why I don’t understand why the explanation says “The argument would still be fine if this were only true of 49% of drivers”
Apologies for the late response, sometimes it’s hard to keep up with the comment queue. Imagine that 49% of drivers (so less than half, i.e. not most) perform slightly worse when supervised. That doesn’t necessarily ruin the rankings because we don’t know what happens to the other 51% – they might improve, stay the same, or also perform worse. The rankings could still hold as long as the relative order of performance doesn’t change.
But if different drivers are affected in different ways (some get worse, some improve, some stay the same), then the rankings could shift, and the best supervised driver might not actually be the best driver overall. That’s why D is necessary – because it ensures that all drivers are affected the same way, making the rankings reliable.
So C isn’t necessary because the argument doesn’t depend on most drivers performing worse, it depends on everyone reacting the same way (D).
I think you’re down the wrong path here. The way you negate most is half. So, original vs. negation. Suppose there’s 1,000 bus drivers.
1. 501 drivers are slightly worse, 499 are not
2. Negation: 500 drivers are slightly worse, 500 are not
The conclusion is that the best drivers will LIKELY be the best under evaluation. It’s a very very weak conclusion. They aren’t demanding perfect individual justice.
If you are taking the LSAT, and your AC breaks, and you perform slightly worse and get one less question right, your measurement is still basically accurate. And add “likely” into the mix and we have even more of an out.
For an answer to be right the negation has to meet a certain minimum bound of strength. If you can negate an answer and you basically change nothing at all then it isn’t right. Hope that helps!
My reasoning for why (C) is wrong is that the author doesn’t necessarily have to assume the supervisor’s impact on the drivers is “slightly worse.” It’s possible the author could assume the impact is “significantly better,” “slightly better,” or even “significantly worse.”
I’m also not convinced that the issue of “only 49% impacted” in the explanation is what makes (C) wrong. I believe that if the best driver is among the 49% impacted, but the second-best driver is among the 51% not impacted, this would actually weaken the argument. This is because it’s reasonable to assume that the best driver and the second-best driver might have very similar performance. As a result, even a slight decrease in performance for the best driver could significantly undermine the argument’s conclusion.
Can someone comment on this? Thanks in advance!