A recent report determined that although only three percent

[as2764]

no, if this were a strengthening/weakening problem, then choice (a) would be a strengthener; if people driving with radar detectors were actually less likely to be caught and ticketed, then the 33%/3% discrepancy described in the passage would actually take on even more significance.

the problem, though, is that you are approaching this problem from the wrong angle: you're trying to approach it as though it were a strengthening/weakening problem.
it's not.

i was trying to classify the answer choice. could you please explain how it's a strengthener?

an assumption is not the same thing as a strengthener!
the vast majority of strengtheners are NOT assumptions!

so, some strengtheners can be assumptions? i have seen this in some cases.

this assumption treats the population of drivers who are ticketed for speeding as a reliable representation of the general population of speeders. although this may seem "obvious" to you, it is still a required assumption!

basically -- the conclusion is saying that the drivers with detectors in their cars have a tendency to speed. and the assumption says that people who were ticketed have an innate tendency to speed, suggesting that drivers with detectors caught for speeding fall into the habitual speeders category?

[RonPurewal]

i was trying to classify the answer choice. could you please explain how it's a strengthener?

you should definitely stay away from "classifying" -- or, for that matter, any form of rules-based formal thinking -- when it comes to strengthening and weakening. the only way you're going to get better at strengthening and weakening questions is by abandoning systematic thinking altogether and developing your intuition. (this is why those questions are there in the first place.)

my explanation of why this is a strengthener is already in the post that you quoted. therefore, for the sake of not repeating myself, here's an analogy:
let's say that, although race X only makes up 3% of the population of some country, people of race X account for 33% of the positive tests for a certain disease in that country.
this answer choice is like saying "people of race X are *less* likely to test positive for that disease" -- if this is true, then the actual discrepancy is probably even larger than 33:3.

so, some strengtheners can be assumptions? i have seen this in some cases.

theoretically, it's possible to have a strengthener that simply validates an assumption about which there is reasonable doubt.
i have not actually seen this -- at least as far as i remember -- but, if it occurred, it would be legitimate.

however, i doubt that you are going to see that sort of thing, as it would blur the lines between assumption questions and strengthening questions.

the whole point of strengthening questions is to take statements that are more or less completely outside the scope of the argument, and then judge how they would strengthen or weaken the argument.

basically -- the conclusion is saying that the drivers with detectors in their cars have a tendency to speed. and the assumption says that people who were ticketed have an innate tendency to speed, suggesting that drivers with detectors caught for speeding fall into the habitual speeders category?

you're overanalyzing; you don't need to make dubious assumptions such as "innate tendency".
basically, this argument is treating X (= people ticketed for speeding) as if it were the same as Y (= people who actually speed all the time).
this X and Y are not necessarily connected, so you need an assumption that connects them. the correct answer does this job.
... and that's it. that is 100% of what is happening here; anything past that point is at best superfluous, and at worst harmful over-analysis.

[as2764]

sounds good. this problem while take some time to sink in, but 'm sure it'll be worth many assumption questions to come!

[RonPurewal]

sweet

[saptadeepc]

Ron - by the time this post would be answered, I would have already taken the test :-)

But, I would like to make of the argument what I understood. If incorrect please correct me for future visitors.

I drew up an analogy

1. I go to "ABC" school - premise 1
2. People who go to "ABC" school are good. - ASSUMPTION
3. Therefore, I 'am good - Conclusion

Similarly, here
1. 3 % equipped with radar and 33% ticketed - premise 1
2. Drivers, who are ticketed are more likely to be exceeding speed - ASSUMPTION
3. Therefore, drivers in discussion ( drivers with radar, who are ticketed ) are more likely to be ticketed - Conclusion

option A - This is opposite of the conclusion. Conclusion says specific are more likely to be ticketed, but the option says generic are less likely. opposite of what we want

option C - This is one of the premise. number of vehicles ticketed were more that's y the percent is 33.

option D - doesnt matter because we are dealing with the number of times a vehicle got ticketed and not how many times 1 vehicle was ticketed. Many doesnt mean most, it could mean some. If we negate this, it doesnt effect our conclusion

option E - irrelevant.

i.e if a set complies to an argument ( ASSUMTION ) , we can say a subset complies to the same argument as well ( CONCLUSION )

[RonPurewal]

Ron - by the time this post would be answered, I would have already taken the test :-)

But, I would like to make of the argument what I understood. If incorrect please correct me for future visitors.

I drew up an analogy

1. I go to "ABC" school - premise 1
2. People who go to "ABC" school are good. - ASSUMPTION
3. Therefore, I 'am good - Conclusion

there is some element of similarity here, but this argument doesn't really work like the one in the problem.
in particular, the argument in the problem depends on statistics and, in particular, on the notion that the distribution of one population characteristic (speeding in the first place) mirrors the distribution of another such characteristic (receiving a ticket for speeding). by simplifying the argument to a basic modus ponens argument about one individual, you've removed most of the essence of this argument.

this is not to say that your argument doesn't work; it certainly does work. (in fact, "x, if x then y, therefore y" is probably the single most common form of logical argument in the world.) it's just that your argument contains fewer elements than the argument at hand.

as for your explanations of the answer choices, those are fairly accurate, except for this one:

option C - This is one of the premise. number of vehicles ticketed were more that's y the percent is 33.

this is incorrect.
this answer choice claims that the total number of tickets given is greater than the total number of cars that have radar detectors (whether those cars' drivers have ever been ticketed or not).
by no means does this have to be true. for instance, the 33 percent statistic could be true even if only three speeding tickets had ever been given -- one to a driver with radar, and two to drivers without radar -- despite the fact that millions of cars might be equipped with radar.

i.e if a set complies to an argument ( ASSUMTION ) , we can say a subset complies to the same argument as well ( CONCLUSION )

you should watch it here, too. this kind of reasoning works here only because this is an assumption question, so you are actually trying to reason backwards -- i.e., you have the argument's conclusion, and you are trying to "back into" the assumption.

if your job is to come up with the conclusion, then the same line of reasoning definitely doesn't work in general. for instance, if swedish men are generally taller than brazilian men, this does not mean that some random group of swedish men will automatically be taller than some random group of brazilian men.

[namnam123]

I find this very hard. Can the evidence be

3 cars with detector-------------------97 cars without detector
1 car(of 3 above) is ticketed------2 cars(of 97 above) is ticketed

is that the evidence which is given.

and from those numbers, we find the assumption B make the argument valid.

is that right?

[RonPurewal]

I find this very hard. Can the evidence be

3 cars with detector-------------------97 cars without detector
1 car(of 3 above) is ticketed------2 cars(of 97 above) is ticketed

is that the evidence which is given.

and from those numbers, we find the assumption B make the argument valid.

is that right?

you're still not addressing the most important distinction in the passage, which is the distinction between
* being ticketed for speeding
and
* being a regular speeder.

like all CR statistics questions, this problem is not really about statistics or numbers. (this is not the quant section!) instead, the problem is about the ideas behind those statistics/numbers.

stacey explained it perfectly earlier in this thread:
post3248.html#p3248

[shwetha_shyam]

Apologize for reopening this thread.

What really baffles me is the conclusion :
Clearly drivers who equip vehicles with radar detectors are more likely to exceed REGULARLY than drivers who do not equip.

Now drivers (speeding tickets) - a) 33% with detector b) 67% w/o detector.

Assumption states : Drivers who are ticketed more likely to exceed REGULARLY than drivers NOT ticketed.

So how can we conclude that drivers who equip vehicles with radar detectors are more likely to exceed REGULARLY than drivers who do not equip - since both equipped and non-equipped drivers are more likely to speed regularly AS LONG as they are TICKETED.

In short, the assumption is targeting to ticketed vs non ticketed drivers while the conclusion is on drivers with and without detectors.

[tim]

what the assumption is actually doing is establishing a correlation between getting ticketed and regular speeding. since we've already established in the first two lines a correlation between radar detectors and getting ticketed, we now have the necessary connection between radar detectors and regular speeding (as seen in the conclusion). does this help? if not, please try working with some actual numbers, and feel free to ask followup questions if necessary..

[gmatwork]

thanks, this is helpful!

[tim]

glad to hear it!

[sachin.w]

Hi ,

I understand B is right.
But here, if we try to apply negation on B, it probably doesn't work or becomes too abstract to understand and check if the conclusion falls apart.

So the takeaway should be that, we shouldn't always apply negation?

[tim]

just because negation is confusing to you doesn't mean it won't work. negation is ALWAYS a valid strategy for assumption problems..

[sachin.w]

ok.
I am trying to get a grip on negating as its a very powerful tool for assumptions.

negating B

(B) Drivers who are ticketed for exceeding the speed limit are less or equally likely to exceed the speed limit regularly than are drivers who are not ticketed.

So conclusion that
drivers who equip their vehicles with radar detectors are more likely to exceed the speed limit regularly than are drivers who do not
falls apart because those who equip their vehicles with radar detectors are the ones who were ticketed 33% of the times.

hope my understanding is right..Please correct me if I am wrong.

Regards,
Sach