Oh yeah, definitely agree!
The two "direct" causal links are the only ones we would really call "causal" regarding A and B.But I am a big fan of "correlation implies causation." It might not be between A and B specifically, but it means we've been able to detect something happening.Sometimes even non-effects, when theory is strong enough, can indicate causation (though then the usual course of action is to control one of the paths to get an effect that you can talk about and publish). For example, you are about to eat an allergen, which you know causes side effects for you with p=1. You take Benadryl beforehand and have no side effects. There is no "effect" there (post state = pre state), but you can feel pretty sure Benadryl had a suppressing action on the allergen's effects (and then you would follow-up with experiments where you ate the allergen without Benadryl or took the Benadryl without eating the allergen to see the positive and negative effects separately).
OP's claim is that intelligence is positively skewed. Counter-points are "most brains are slightly worse" (Donald Hobson) and "you oversample the high-intelligence people, so your claim is biased because of availability" (Ericf).Both of these counter-points agree with, rather than disagree with, lsusr's point. Most brains are slightly worse implies positive skew and to the extent that lsusr oversamples high-intelligence people, they are underestimating how positively skewed intelligence is yet still conclude it is positively skewed (caveat: as Donald Hobson says, the measurement approach can be really important here, but for the sake of argument let's say lsusr is talking about latent intelligence, and our measures just need to catch up with the theory).Ericf also makes another interesting point- "variation in low intelligence is less identifiable than variation in high intelligence," 160 vs. 130 IQ people will act differently, but 40 vs. 70 IQ people won't so much, or at least the IQ test is better at delineating on the high end than low end. I am no expert on the measurement of intelligence, but this point probably shouldn't just be taken at face value- for example, individuals with Down's syndrome consistently have IQs less than 70 and getting below 70 is rare, as expected since IQ is designed to be Gaussian. But the implication of that is that as rare (and therefore difficult to dig into) as low IQs are, high IQs are...equally rare (and therefore difficult to dig into).I agree that OP's claim should also be subjected to scrutiny -simply saying intelligence is positively skewed doesn't make it so- but I also don't find the present set of counter-points either that contradictory or that convincing either. Just my two cents.
FWIW, number sense is definitely a thing in psychology.
FDA Dr. Peter Marks's reply either indicates his own misunderstanding or that something is wrong with the FDA report! In Table 15 of the FDA's Moderna report, they report efficacy "in Participants Who Only Received One Dose" (emphasis added and the N's are correctly not the full trial's N). 80% (95% CI: 55%, 93%) is a nice round number to tell people, but also we assess two-dose efficacy only after 14 days anyway, so the truly comparable number is 92% (95% CI: 69%, 99%). Now if there are other reasons we shouldn't trust those numbers, I'd love to see them. They caveat it with it's not necessarily 80+% effective forever since they only observed single-dosers for a median of 28 days, and the N is definitely lower but still 1000 per group (which is why the confidence intervals are wide). But that gives us pretty high confidence that 14 days after the first dose, the vaccine is effective enough to warrant JABS IN ARMS!
It's between-subjects, these aren't real probabilities for individuals. But from a Bayesian standpoint it gives you useful base rates with which to assess risk.
One way to "rewire" your brain is to wire in a quick check- how does selection/stratification/conditioning matter here?But perhaps most important is to think causally. Sure, you can open up associations, but, theoretically, do they make sense? Why would obesity, conditional on having cardiovascular disease, reduce mortality? Addressing why rather than leaping to a bivariate causal conclusion is important. This is why scientists look for mechanisms and mechanism-implicating boundary conditions.
I'm having trouble with it too and I think Zvi misinterpreted it as well- the far right column is the VE.
Indeed, these aren't controlled experiments at all, but sometimes they are also not policy-sneaking. Sometimes they are just using the phrase "experimenting with" in place of "trying out" to frame policy-implementation. At that point, the decision has already been made to try (not necessarily to assess whether trying is a good idea, it's already been endorsed as such), and presumably the conditions for going back to the original version are: 1) It leads to obviously-bad results on the criteria "management" was looking at to motivate the change in the first place or 2) It leads to complaints among the underlings.The degree of skepticism, then, really just depends on your prior for whether the change will be effective, just like anything else. Whether there should have been more robust discussion depends either on the polarity of those priors (imagine a boardroom where someone raises the change and no one really objects vs. one where another person suggests forming an exploratory committee to discuss it further), or on whether you believe more people should have been included in the discussion ("you changed the bull pen without asking any of the bulls?!"). It has little to do with the fact that it was labeled an experiment, since again, it's likely being used as business-speak rather than as a premeditated ploy. I would love to have data on that though- do people who specifically refer to experimentation when they could just use a simpler word tend to use it innocuously or in a sneaky way?