lina2020 wrote:Hi PowerScore,
I confidently chose C as my answer and even after reading through the explanations on this forum, I'm having trouble understanding why D is correct. For quick reference, I pasted the question below as it typically takes a couple of days to get a response.
My thought process with C is that there are a numbers/percentages flaw in this argument --> although less than 15 PERCENT of those in migrating populations are infected, perhaps the ACTUAL NUMBER of those migrating are much higher than those not migrating, in which case it would expose a flaw on the causal conclusion "that migrating allow monarch butterflies to avoid these parasites" because in fact there could be a larger number of those migrating affected than those non-migrating. Would you please explain in detail what I may be missing here? I somewhat understand the reasoning for D being reverse causation but I feel like my explanation could possibly indicate a second flaw with this argument. Any insight on answers C and D is appreciated!
Also, would you mind pointing me in the direction of 2-3 other similar reverse causation LR questions that I could look at to better understand this concept? I'm a current PowerScore student so if you mention the PrepTest number and year, I can locate the question and answers. Thank you!
Stimulus:
Monarch butterflies must contend with single-celled parasites that can cause deformities that interfere with their flight. In populations of monarch butterflies that have not migrated, as many as 95 percent are heavily infected by the parasites, while less than 15 percent of those in migrating populations are infected. This shows that migrating allows monarch butterflies to avoid these parasites.
C. populations of monarch butterflies that have not migrated are much larger than migrating populations
D. monarch butterflies infected with parasites are typically unable to migrate
I know I'm responding almost 2 months after this post, but hopefully this can be helpful for anyone with similar reasoning. I can see how someone can fall into this trap.
The reason C doesn't work is that the population size doesn't affect the argument like you're thinking. If the stimulus had used just the numbers in both of the moth groups, then the answer choice would be a lot more attractive, but ultimately its about percentages.
We will use the argument's reasoning for this analogy too so it makes more sense. If 95% of group A (who doesn't own a blue car) is infected with a virus, and 15% of group B (who do own a blue car) is, and I conclude that group B doesn't get infected as much because they drive blue cars, you'd probably tell me I'm taking crazy pills and my conclusion makes no sense, regardless of what numbers I'm using for those groups. Hopefully, you can see the analogy to the argument at hand.
Telling me that 19 people in A (who don't own blue cars) got infected, but 15 people in B (who do own blue cars) got infected, therefore doing X activity explains why B is infected less isn't a great argument, and it's lacking any additional support. If you responded to my analogy above with, "Well, what if there are more people who don't drive blue cars than those who do", you wouldn't be pointing out the error in the conclusion that is too strong, you're suggesting the size of the groups is an important factor and that there is possibly some truth to the stimulus argument. Rather, you should be attacking support for the claim that the specific activity of migrating lets them avoid the parasites.
Now if you replied to the argument, "Owning a blue car is irrelevant to whether they get sick or not, what if the only people who can drive blue cars are super rich people with lots of money to afford a vaccine/treatment in the first place", you'll see it's a better way of attacking the reasoning, and ultimately what the flaw is. Seeing a very obvious causal argument such as this one should be setting off alarms in your head to hone in on an answer choice that reflects that the causality stated is for another reason, or that there is only a correlation, or even reverse causation (which is what D is doing)