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• 2 Likes

  canihazJD Alum Member Sage ⭐

  February 2021 edited February 2021 8486 karma

  D just says bag limits are based on weight not number. CPUE is based on number regardless.

  E says they are a lot better now at finding the sharks. So before I was just kind of randomly dropping nets wherever and hoping to get sharks, but now I know exactly where they are and can specifically target those areas. If my catch rate is still the same, that would indicate that population is dropping not constant.
• moonstars5678 Member

  January 2022 166 karma

  @canihazJD Do you mind explaining why if the catch rate is still the same, why the population would be dropping and not constant?

  Is it because if we were just throwing around random nets in 1973 and capturing 100 sharks (vs today when we have very targeted radars that can directly find the sharks and we still ended up with 100 sharks), then chances are there was a higher shark population in 1973 because the factor of randomness?

  Like, if I blindfolded myself and tried to find only the blue M&Ms in a full jar of M&Ms ended up with only 10, vs if I unblindfolded myself and specifically tried to find the blue M&Ms and still only ended up with 10, then just due to probability, there would've been more blue M&Ms in general in the first case. Is that the right way of thinking about it?

  help
• canihazJD Alum Member Sage ⭐

  January 2022 edited January 2022 8486 karma

  @moonstars5678 I'll try to make your M&M analogy better...

  Imagine a standard jar of M&Ms with equally distributed colors that you just grab into blindly trying to get as many blues as possible. You get 10.

  Now imagine another jar but now you somehow have the ability to attract only blues into your hand. You still get 10. This would indicate that this jar likely has comparatively less blue than the other. Because youre super good at finding them now but still came out with the same amount. If they both had the same amount you would have expected to come out with more.
• 1 Like

  KevinLuminateLSAT Alum Member

  January 2022 984 karma

  @moonstars5678

  I think it’ll be helpful to get a more full picture of what’s going on, as well as clarify our job on this question type. And to do that, let me develop your M&M example a bit.

  Let’s say we’re measuring how many blue M&Ms you collect per hour from stores in your city.

  What factors affect your blue M&M collection rate?

  • How quickly you can identify places with M&Ms
    
    
    • All else equal, the better you are at identifying candy shops, grocery stores, and other places with M&Ms, the more M&Ms you’ll collect per hour.
  • How quickly you can travel to these places
    
    
    • All else equal, the faster you can travel between M&M-having locations, the more you’ll collect per hour.
  • How many M&M bags are available at these stores
    
    
    • All else equal, the more bags per store, the more you’ll collect per hour.
  • How long it takes to buy the M&Ms at the store (assuming you can’t just steal the M&Ms)
    
    
    • All else equal, the faster you can get through the checkout, the more you’ll collect per hour
  • How quickly you can open M&M bags
    
    
    • All else equal, the faster you can open the bags, the more you’ll collect per hour
  • How quickly you can pick out the blue M&Ms (assuming that this kind of selection is necessary - it’s possible that a faster way of collecting the blue M&Ms is simply collecting all of them since you’ll still have the blue ones)
    
    
    • All else equal, the better you are at picking out the blue ones, the more you’ll collect per hour
  • The likelihood of losing your M&Ms (dropping them in sewers or getting robbed)
    
    
    • All else equal, the lower your chance of losing your M&Ms, the higher your blue M&M collection rate.

  This is of course not an exhaustive list, and some of these factors can be broken down into even more factors - how quickly you can travel between stores with M&Ms is itself a function of how many such stores exist nearby, the traffic at the time you are traveling, and many others.

  The point is, the blue M&M collection rate is a product of many different factors.

  Now let’s say you get an argument that looks like this:

  My hourly blue M&M collection rate was the same in 2019 as it was in 2020. Therefore, the number of stores with M&Ms in my city stayed about the same from 2019 to 2020.

  This argument is flawed because it’s overlooking the possibility that the number of stores with M&Ms actually did change, but that other factors relevant to the M&M collection rate changed in a way that just counterbalanced the effects of the change in store number. In other words, many factors that go into the rate could have changed but we still end up producing a net zero change in the collection rate.

  So if we’re asked to weaken the argument, a correct answer would be anything that suggests another factor besides the number of stores changed from 2019 to 2020. It doesn’t matter what other factor that is, and it doesn’t matter the direction of the change in that factor. If, for example, purchasing M&Ms at stores took much longer in 2020 than in 2019, then you would expect, all else equal, the blue M&M collection rate to decrease. But since the collection rate stayed the same, then that means another factor - such as the number of stores - changed in a way that increased the collection rate. Or if, for example, we improved how quickly we traveled between candy shops and grocery stores from 2019 to 2020, then all else, equal, we’d expect our collection rate to increase. So the fact that it stayed the same means that some other factor - such as the number of stores - changed in a way that decreased the collection rate.

  Does pointing out that another factor changed from 2019 to 2020 actually prove or suggest that the particular factor the argument's conclusion is about - the number of stores - actually changed? No, it doesn't. But it would still weaken the argument by showing that the premise - equal collection rate in 2020 as in 2019 - doesn't automatically prove the number of stores stayed the same. In other words, it raises the possibility that the number of stores could have changed despite the net zero change in collection rate.

  ---

  Turning to the shark question, what factors go into the catch per unit of effort?

  • Effectiveness of the nets (lower malfunction rates, lower rate of sharks escaping)
    
    
    • All else equal, nets that are more effective at catching sharks should increase the CPUE
  • Efficient targeting (using nets where sharks are likely to be found or are for other reasons easier to catch - maybe sharks are slower at certain times, swim closer to the surface, etc.)
    
    
    • All else equal, more efficient targeting should increase the CPUE
  • Shark population around South Australia
    
    
    • All else equal, more sharks in the area should increase the CPUE (you wouldn't catch any sharks if there were 0 around, but if there are millions, you'll catch more)
  • Shark’s escape ability
    
    
    • All else equal, decreased shark escape ability should increase the CPUE (perhaps they’re sluggish due to pollution or perhaps they grew larger and less dextrous and can’t squeeze out of nets)

  The author of the argument thinks that the constant CPUE since 1973 means that the shark population is about the same. But we can weaken this argument by showing that any other factor relevant to CPUE changed since 1973. For example, if we improved our ability to identify locations where sharks are present, then all else equal, our expected CPUE should go up. So the fact that the CPUE stayed the same means that at least one other factor must have changed in a way that decreased the CPUE - such as, for example, a lower shark population.

  That’s why (E) weakens. It’s important to note that (E) doesn’t actually suggest that the shark population decreased. For example, upon further investigation of marine life, it may turn out that even if (E) is true, it was some other factor that decreased the CPUE, such as improved shark escape ability (maybe they’re learning how to avoid nets), and that the overall shark population did in fact still stay the same, just as the author thought. But (E) still weakens the argument by showing that the author’s premise - constant CPUE since 1973 - doesn’t automatically show that the population stayed the same.

  If it helps, we can also think of what’s going on here in terms of conditional principles.

  In the case that a particular measurement is a product of factor X, Y, Z, and many other factors…

  If factor X changes, AND all other relevant factors stay the same -> then the measurement should change, too.

  That principle should, I think, make intuitive sense. What’s the contrapositive of that principle?

  If the measurement does NOT change -> then factor X did NOT change OR not all other relevant factors stayed the same. (We switched both sides of the conditional, negated both terms, and switched the “AND” to an “OR”.)

  The shark argument tries to use this contrapositive. It begins with the premise that the measurement did not change. Then it thinks that this proves a particular factor, shark population, did not change either. But do you see how it ignored the other part of the “OR” in the contrapositive - the possibility that other relevant factors changed? That’s what (E) is pointing out, and that’s why it weakens the argument. And I’ll reiterate that (E) doesn’t have to suggest that the population changed in order for it to weaken the argument. Our burden on a weaken question is merely to show that the author’s conclusion does not automatically follow from their premise.

  If you’re into conditional statements, here’s one last example that might be helpful in understanding this last point about our burden on weaken questions. Let’s say the following statement is true:

  If A is true → then B or C is true.

  Now we get someone who tries to use that statement to make the following argument:

  A is true.

  Therefore, B is true.

  Does it make sense that a perfect weakener would be “C is true”?

  How would you respond, however, if someone were confused and asked “But how does C being true show that B is NOT true?”

  The explanation is that C’s truth doesn't in any way make B untrue or even make B less likely to be true. In fact, it’s entirely possible B is true. But C’s truth still weakens the argument by showing that the author’s conclusion was unwarranted - we are not compelled to accept that B is true merely because A is true.

  So if you’re wondering how (E) in the shark problem proves that the shark population is dropping - perhaps the explanation is actually that it doesn’t, nor does it have to in order to be the correct answer. It’s merely pointing out that we are not compelled to accept the the shark population is the same merely on the basis of the constant CPUE.
• moonstars5678 Member

  January 2022 166 karma

  @canihazJD and @KevinLuminateLSAT - you two rock. Thank you so much - this helps so much.
• chrleesj Core Member

  February 2022 32 karma

  Thank you to @moonstars5678, @canihazJD, and @KevinLuminateLSAT for your comments!! I was having a lot of trouble understanding why E was correct. Y'all helped me realize I was making a lot of assumptions