Estimating the IQ of Richard Hanania

Richard Hanania graduated from the university of chicago law school, where the median LSAT is 173 (25th percentile: 169, 75th: 175). This means that scores are distributed with a mean of 173 and SD of roughly 4. According to my neko function, the distribution of the LSAT is a mean of 152 with a standard deviation of 10.1. In z-notation, this means that the distribution of the UC LSAT scores has a mean of 2.1 and a standard deviation of 0.396. Given that these LSAT test takers are already an elite sample, this is closer to 2.4 and 0.356 if we assume the LSAT takers have an IQ distribution identical to that of all americans with teritary education.

I remember a substack post where Hanania claimed to be in the 99th percentile of IQ and disagreeableness at the same time - I can’t remember the particular post, but I don’t think I’m blowing anybody’s mind when I say that Hanania thinks very highly of his intelligence. (edit: actually, it was 99.9 - true discipline sent me the link)

Simulation will be conducted assuming that IQ and LSAT correlate at 0.84 within the general population, and IQ and self-percieved intelligence correlate at 0.4.

The results of the simulation indicated that Richard Hanania has an IQ of roughly 130.5, with a standard error of 9.4

g <- rnorm(60000000)
ls <- 0.84*g + rnorm(60000000)*sqrt(1-0.84^2)
cs <- 0.3*g + rnorm(60000000)*sqrt(1-0.3^2)

subby1 <- data.frame(ls, cs)
subby1$g = g

#monte carlo simulation made to mimic the LSAT distribution of the UC
randos <- rnorm(60000000, mean=0, sd=0.2)
subby1$sel = subby1$ls - randos

subby2 <- subset(subby1, subby1$sel > 2.15)
mean(subby2$ls)
sd(subby2$ls)

subby3 <- subset(subby2, (subby2$cs < 3.2) & (subby2$cs > 3))
mean(subby2$g)
sd(subby2$g)