#18 - An article claims that many medical patients have

[Adam Tyson]

I tend to think of "supporter" and "defender" as strategies, KG, rather than as distinct question types. Any supporter can also be a defender. In this case, I might attack this argument by saying "that analogy might not be good, because in this case people might remember both outcomes equally." The assumption that defends against that attack is "in this case there is a difference in which thing people tend to remember." Or put another way, the author has to assume that a correct prediction is more likely to be remembered than an incorrect prediction.

There doesn't have to be anything airtight about the argument regardless of which type of assumption strategy you apply, and a correct assumption answer doesn't need to perfect the argument because there could still be many other assumptions required by the argument. A defender just removes some problem; it doesn't necessarily remove all problems, the way a Justify answer must do. A supporter connects the premises to the conclusion in some way, but the argument could still be imperfect even after closing that gap.

[lounalola]

I understand why B is correct. However I am a bit confused about the answer. The article about the patients being able to predict changes in their medical status never mentioned medical staff. I don't really understand why the medical staff are relevant to this argument, could the answer not just say that "patients are less likely to remember incorrect predictions"?

[Adam Tyson]

It's about making the comparison better, lounalola, and in the case of babies and full moons, it was the maternity staff who had biased memories, not the patients. Also, it wouldn't really matter if a patient remembered their correct prediction and forgot their incorrect prediction, unless a bunch of patients all talked to each other about it. But the medical staff sees lots of patients come and go, and if they are remembering a lot of people making accurate predictions, but forgetting a lot of people making inaccurate predictions, that would lead to a mistaken or biased view about the accuracy of those predictions.