This is a special post for quick takes by Eli Tyre. Only they can create top-level comments. Comments here also appear on the Quick Takes page and All Posts page.
I'm mostly going to use this to crosspost links to my blog for less polished thoughts, Musings and Rough Drafts.
"The mean correlation of IQ scores between monozygotic twins was 0.86, between siblings 0.47, between half-siblings 0.31, and between cousins 0.15."
I'd like to get an intuitive sense of what those quantities actually mean, "how big" they are, how impressed I should be with them.
I imagine I would do that by working out a series of examples. Examples like...
If I know that Alice has has an IQ of 120, what does that tell me about the IQ of her twin sister Beth? (What should my probability distribution for Beth's IQ be, after I condition on Alice's 120 IQ and the 0.86 correlation?) And how does that contrast with what I know about her younger brother Carl?
What if instead, Alice has an IQ of 110? How much does that change what I know about Beth and Carl?
How do I do this kind of computation?
[I'm aware that herritability is a very misleading concept, because as defined, it varies with changes in environmental conditions. I'm less interested in heritability of IQ, in particular, at the moment, and more in the general conversion from correlation to Bayes.]
In theory, you can use measured correlation to rule out models that predict the measured correlation to be some other number. In practice this is not very useful because the space of all possible models is enormous. So what happens in practice is that we make some enormously strong assumptions that restrict the space of possible models to something manageable.
Such assumptions may include: that measured IQ scores consist of some genetic base plus some noise from other factors including environmental factors and measurement error. We might further assume tha... (read more)
I'm mostly going to use this to crosspost links to my blog for less polished thoughts, Musings and Rough Drafts.