Investigation of Talebs claims about IQ using real data.

Inside science

Author

Jonatan Pallesen

Published

June 16, 2019

Introduction

In this post I look into the document by Nassim Taleb: IQ is largely a pseudoscientific swindle. Most of the document is just a rant, but he does make some quantitative claims, which is good. I investigate the veracity of these claims using real data.

[Edit: The quotes below were taken verbatim from Taleb’s document as it was written when this post was uploaded. He has since changed some claims in his document.]

Variance explained by IQ

Claim:

[IQ] explains at best between 2 and 13% of the performance in some tasks

I already have prepared the WLS data set here, and it has a good IQ test, so I will use that. Lets look at the regression of grades on IQ.

Call:
lm(formula = grades_std ~ iq_std, data = wls %>% drop_na(iq_std,
grades))
Residuals:
Min 1Q Median 3Q Max
-3.5310 -0.5248 0.0204 0.5307 3.4381
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.005302 0.008180 -0.648 0.517
iq_std 0.595438 0.008161 72.965 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8024 on 9622 degrees of freedom
Multiple R-squared: 0.3562, Adjusted R-squared: 0.3561
F-statistic: 5324 on 1 and 9622 DF, p-value: < 2.2e-16

The adjusted R^{2} is 0.356, so IQ explains ~35% of the variance of grades in this data set. Taleb defenders may say that things such as school grades don’t matter in real life. But whatever your position on this, the situation is that he made a specific claim about how much IQ explains at best, which was off by a large factor.

IQ selection vs random selection

Claim:

Turns out IQ beats random selection in the best of applications by less than 6%

He hasn’t specified what exactly he means by this. But I’m pretty sure he is thinking of a comparison like the one he talks about in this tweet:

So I will analyze something this, a stylized example with assumptions similar to these. In the real world, you will likely have other information that complicates this picture, such as education credentials; but we ignore that for now.

I create two normally distributed variables, with correlation 0.5, and call them IQ and performance.

Code

n <-10**8X0 <- MASS::mvrnorm(n=n, mu =c(0,0), Sigma =matrix(c(1,0.5,0.5,1),2,2))df <-tibble(IQ = X0[,1], performance = X0[,2])df %>%summarise(cor =cor(IQ, performance), n =n(),mean_IQ =mean(IQ),sd_IQ =sd(IQ),mean_performance =mean(performance),sd_performance =sd(performance) ) |>gt()

cor

n

mean_IQ

sd_IQ

mean_performance

sd_performance

0.5000615

100000000

3.162788e-06

1.000123

0.0001018527

1.000056

The probability of hiring an employee with above average performance, if you hire an employee with above average IQ is:

The probability if there was 0 correlation is of course 0.5. So using IQ as a criteria beats random selection by 16.67% (as Taleb also found). This is a little more than 6%, but that is a detail. More importantly, this example is a theoretical use case where using IQ testing is not so useful. To look at something a little more realistic, let’s say a company wants to avoid people with a performance more than 2 standard deviations below the mean. (Perhaps such employees have a risk of causing large harm, which could for instance be an issue in the military.) And we again compare admitting people at random vs only taking applicants with above average IQ.

If the company admits people at random, we get this proportion of people with a performance more than 2 SD below the mean:

In other words, using IQ selection criteria in this case beats random selection by more than 450%. Quite different than the claimed 6%.

IQ and income

Claim:

There is no correlation IQ/Income above 45K

He makes this claim based on this image:

The image is from this paper, which gets its data from NLSY79. This data is also public, so we can again check for ourselves. Description of the preprocessing I perform is described here.

The plot looks similar to the one from the paper, but somewhat different, because I use the average income values for a period of 10 years, instead of that from a single year.

Another way to make the relationship clearer is to plot the distribution of income for seperate IQ groups. Plotting the distribution avoids the issue of the plot becoming unclear and confusing due to overlapping points.

The plot below again plots only people with income above $45k per year. The difference IQ makes is clear, both in the median value, and the length of the tails.

[The income data] truncates the big upside, so we not even seeing the effect of fat tails

It is correct that in the presence of fat tails the correlation is uninformational in predicting expected income. And the data from these studies does not give good information about such tails. However, it is highly possible that the effect of IQ is even more impactful in the tails, rather than less. For example, Bill Gates and Paul Allen scores 1590/1600 on their SATs, which for SATs at that time meant extremely high IQs. Since Bill Gates is also in the tail of wealth and income, this indicates that the overall impact of people in the tails is to accentuate rather than diminish the importance of IQ.

Correlation only for low IQ

IQ mostly measures extreme unintelligence

He claims that IQ has high correlation with traits at low IQ, but that this correlation falls drastically at above median IQ, as illustrated in this figure from the post:

(Edit: In case this is not clear, Taleb uses the above figure as an illustration, not as a precise empirical claim.) It’s quite easy to look into the data to see whether this actually is the case. We can use the same two data sets used above.

If we plot IQ vs log(income), that will tell us how large a percentage increase in income each extra IQ point gives. Then we can plot a local regression (loess) and compare that to a linear regression. If the relationship between IQ and income radically diminished at above average IQ, then we would expect the loess regression line to approach horizontal. However, this is not what we see - instead the two regression lines follow each other quite closely. It is true that the percentage increase in income per IQ point is a little higher for lower IQs, but it never stagnates.

An alternative illustration is shown below, using the WLS grade-IQ relationship:

Code

plot_smooth <-function(df, above_median, color){stat_smooth(aes(color = color), data = df %>%filter(iq_above_median == above_median), method="lm", se=F, fill=NA, formula=y ~ x, size=1.2) }wls %<>%mutate(iq_above_median = iq100 >100)wls %>%ggplot(aes(y = grades, x = iq100)) +geom_jitter(alpha =0.05) +plot_smooth(wls, F, "turquoise4") +plot_smooth(wls, T, "blue") +scale_color_discrete(name ="Relationship, IQ and grades", labels =c("Above median IQ", "Below median IQ")) +labs(x ="iq")

We see clearly that the above 100 IQ line has a slope that is not horizontal, meaning IQ correlates with grades even at higher than median IQ. In fact the slope is about the same as that for the regression line of students with below median IQ.

In a third illustration, we can look at the distributions of incomes for high, low and median IQs. Both increases in IQ moves the median of the income distribution and enlarges the tail.

All the claims from the article that I looked at, that can be interpreted as something specific and tested in a real data set, turned out not to be correct. If Taleb hadn’t blocked everyone who disagrees with him, perhaps he would have found out about this, and not published a post with all these incorrect claims.