I don’t think the relevant value here is P(goes to school | smart person). I think it’s P(smart person | goes to school). The latter seems much higher for Ivies (Ivies = top ranked schools) than anywhere else, except places that excel in a certain area (e.g., CMU, although this might also be top ranked).
I think the most obvious use case is EA recruitment/outreach, particularly for tasks that smart people are in theory better at (e.g. technical research most clearly, but I think many other tasks too).
P(smart person | goes to school) seems most important for choosing whether you want to do campus outreach programs in university A vs university B. But P(goes to school | smart person) is relevant for the relative weighting of campus outreach vs. other programs that doesn’t rely on this pre-filter.
If there’s a lot of smart people outside of elite universities, you might want to differentially do more EA outreach that captures this market (e.g. online outreach, EAGs, or retreats/conferences that have their own selection process that don’t piggyback on elite university outreach) . If there aren’t, then you don’t want to do this.
I agree. I think it’s also worth pointing out that P(smartperson|goestoschoolX) is a different metric from Count(smartperson|goestoschoolX) (the total number of people matching some criteria). One takeaway I got from the post is that while the probabilities might still be different between schools (“75th percentile SAT score falls very gradually”), the number of “smart” students might be comparable at different schools because many non-elite schools tend to be larger than private elite schools (further, since there are also many more non-elite schools, I might expect Count(smartperson|goestonon–eliteschool)>Count(smartperson|goestoeliteschool) but that’s besides the point).
Practically, for EA outreach, this maybe implies that university outreach might be harder at big non-elite-but-still-good schools: as Joseph points out below, the median student might be less qualified, so you’d have to sample more to “sift” through all the students and find the top students. But EA outreach isn’t random sampling—it appeals to certain aptitudes, can use nerd-sniping/self-selection effects, and can probably be further improved to select for the most smart/agentic/thoughtful/truth-seeking/whatever you care about students, and there might be a comparable number of those at different universities regardless of elite-ness. If the heavy tail of impact is what matters, this makes me update towards believing EA outreach (and students starting EA groups) at non-elite-but-still-good universities could be as good as at elite universities.
You might no longer endorse it, but I (in a weaker form) do!
I’d bet there’s a positive correlation between SAT/IQ/whatever and, say, the dummy variables “has completed intro EA fellowship” controlling for campus attended
Though my intuition and credence is way stronger for more normal, bigger school with high SAT variance than top 10 unis and small liberal arts colleges.
To be clear the reason I unendorsed this comment is that I think it was covered in the comment I was replying to and it was very bad on my part not to read the comment fully and coherently before replying. Checking the EA forum when I’m dead tired isn’t the best idea.
If we set aside the social jockeying and ego boosting, to me the obvious relevance is for hiring/selecting employees. The primary reason I would care whether or not somebody went to a fancy school would be dependent on to the extent that this information can tell me whether or not they would be successful in a particular job I am hiring for. To the extent that it is predictive of success on the job, then I’d want to know.
My gut tells my that it is only slightly helpful, but I don’t have any evidence to support that gut feeling: I think that there are few idiots at the Ivy Leagues, but there are lots of smart people outside of the Ivy Leagues. My rough mental model is that if I were to filter job candidates based on school ranking and look only at candidates who attended the top 25 or top 50 schools, then I would successfully eliminate some bad fit candidates (in a simplistic model in which we can condense success on the job down to a single metric, maybe I’d eliminate the bottom 10%). But I suspect that I’d also end up eliminating a lot of really good candidates from the top 50%.
Probably something like how much effort should be spent on building EA groups at said University. I agree with the examples Linch gives for where this wouldn’t be relevant.
I don’t think the relevant value here is P(goes to school | smart person). I think it’s P(smart person | goes to school). The latter seems much higher for Ivies (Ivies = top ranked schools) than anywhere else, except places that excel in a certain area (e.g., CMU, although this might also be top ranked).
I think the most obvious use case is EA recruitment/outreach, particularly for tasks that smart people are in theory better at (e.g. technical research most clearly, but I think many other tasks too).
P(smart person | goes to school) seems most important for choosing whether you want to do campus outreach programs in university A vs university B. But P(goes to school | smart person) is relevant for the relative weighting of campus outreach vs. other programs that doesn’t rely on this pre-filter.
If there’s a lot of smart people outside of elite universities, you might want to differentially do more EA outreach that captures this market (e.g. online outreach, EAGs, or retreats/conferences that have their own selection process that don’t piggyback on elite university outreach) . If there aren’t, then you don’t want to do this.
I agree. I think it’s also worth pointing out that P(smart person|goes to school X) is a different metric from Count(smart person|goes to school X) (the total number of people matching some criteria). One takeaway I got from the post is that while the probabilities might still be different between schools (“75th percentile SAT score falls very gradually”), the number of “smart” students might be comparable at different schools because many non-elite schools tend to be larger than private elite schools (further, since there are also many more non-elite schools, I might expect Count(smart person|goes to non–elite school) >Count(smart person|goes to elite school) but that’s besides the point).
Practically, for EA outreach, this maybe implies that university outreach might be harder at big non-elite-but-still-good schools: as Joseph points out below, the median student might be less qualified, so you’d have to sample more to “sift” through all the students and find the top students. But EA outreach isn’t random sampling—it appeals to certain aptitudes, can use nerd-sniping/self-selection effects, and can probably be further improved to select for the most smart/agentic/thoughtful/truth-seeking/whatever you care about students, and there might be a comparable number of those at different universities regardless of elite-ness. If the heavy tail of impact is what matters, this makes me update towards believing EA outreach (and students starting EA groups) at non-elite-but-still-good universities could be as good as at elite universities.
A convenient way to sift is focusing on honors colleges within universities—there is some discussion on that here.
The smartest people will always gravitate toward the EA group, you don’t actually have to sift through all the students to find them.
You might no longer endorse it, but I (in a weaker form) do!
I’d bet there’s a positive correlation between SAT/IQ/whatever and, say, the dummy variables “has completed intro EA fellowship” controlling for campus attended
Though my intuition and credence is way stronger for more normal, bigger school with high SAT variance than top 10 unis and small liberal arts colleges.
To be clear the reason I unendorsed this comment is that I think it was covered in the comment I was replying to and it was very bad on my part not to read the comment fully and coherently before replying. Checking the EA forum when I’m dead tired isn’t the best idea.
I’m curious why you believe this
Hmm, I think it depends on what the relevance is for. What do you have in mind?
If we set aside the social jockeying and ego boosting, to me the obvious relevance is for hiring/selecting employees. The primary reason I would care whether or not somebody went to a fancy school would be dependent on to the extent that this information can tell me whether or not they would be successful in a particular job I am hiring for. To the extent that it is predictive of success on the job, then I’d want to know.
My gut tells my that it is only slightly helpful, but I don’t have any evidence to support that gut feeling: I think that there are few idiots at the Ivy Leagues, but there are lots of smart people outside of the Ivy Leagues. My rough mental model is that if I were to filter job candidates based on school ranking and look only at candidates who attended the top 25 or top 50 schools, then I would successfully eliminate some bad fit candidates (in a simplistic model in which we can condense success on the job down to a single metric, maybe I’d eliminate the bottom 10%). But I suspect that I’d also end up eliminating a lot of really good candidates from the top 50%.
Probably something like how much effort should be spent on building EA groups at said University. I agree with the examples Linch gives for where this wouldn’t be relevant.