So there is a new Dunning-Kruger paper out by Gignac and Zajenkowski. It goes like this:
Dunning Kruger pattern is trivial given 1) people overestimate themselves, and 2) self-estimate x criterion value is r < 1.00. I agree, I wrote that years ago.
So they collect some new data, typical weird format students self-rating and Raven's test. Looks like the usual deal.
A nice twist is that they realize the DK claim is a test for heteroscedasticity (what? inconstant variance). Well, I recently spent a lot of time thinking about this and they posted their data on OSF, so all is good, time for re-analysis!
So downloading their data, scoring it by simple z-transform, and computing the 10/90th centiles, I get this plot which shows quite clearly there is HS like DK model says, on the low end.
This plot is made using the neat qgam() function, quantile generalized additive models, allowing us to capture nonlinear heteroscedasticity if such exists and visualize it easily.
But we are not lazy, so we apply some tests that give p values & effect sizes. Now, I also came up with some 'new tests' and metrics, so let's try them!
They confirm the visual results: there data have good evidence of HS, and it's somewhat nonlinear. 2/3 old tests agree.
Oh, by the way, for that point about writing it years, ago, I built a simulator that you can play around with!
So I talked with Gignac about this, and he pointed out that I plotted the data the wrong way around. Good point! If we do it the right now around, we get these.
So there is some upwards tick at the very low end, but these are just a few lizardmen out of 2400 people.
Here's the same but with the 10/90th quantiles. There's basically nothing to see here. It's very linear and homoscedastic.
So, this n=2400 replication (about 2.5x sample size) finds essentially the same result when done properly.
Main difference is that my cors are stronger.
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"Once dull, always dull". Special education classes have very good metrics for teacher education and students per teacher, but they don't seem to improve much from this extra effort. So Terman infers from this that these factors cannot be of great importance.
New study of 2700 Indian whole genomes shows that Indo-European/Yamnaya/Steppe is quite low, about 15%. It is a bit higher in the north (almost 20%) and among those who speak IE languages, but not impressively so. Too much mixing since the arrival.
Inbreeding is strong. The median person had identical blocks in their genome at the level suggesting their parents were 3rd cousins, whereas the human average is about close to 4th cousins (Africans less).
Annoyingly, the data is not public, even though American taxpayers paid for this. Can't wait for Trump admin to maybe do something about this abuse (inb4 fell for it again award).
Not everybody commit scientific fraud at the same rate. Let's look at some data. First retraction rates.
We can also look at the top list of most fraudulent researchers ever (so far those that were caught). Note: data from 2019 list. There's quite a few non-Europeans.
This is important because we want a per capita measure of sorts. For highly regarded journals, European built countries produced about 75% of science (Nature Index), but maybe 30% of top fraudsters.
You have maybe seen this plot. It shows ratings of races in USA by race. The key result is that each race favors their own, except for Whites who are apparently race-blind.
Since it only covered a single year, 2020, I wondered if this would replicate across years of data. So I downloaded the data for 1964-2024.
So we see that the result replicated (I didn't use survey weights). And indeed, in 2020 and 2024, Whites have about zero ethnocentrism.
But this pattern among Whites is more complex. Here are the ratings by political ideology (self-rated 1-7 scale). The below average attitude towards fellow Whites is concentrated on the left, just as @ZachG932 found before. Especially the far left.
Mental issues roughly follow a hierarchical pattern, like cognitive abilities, like a general factor on top. At least, statistically.
The motivating factors behind this approach compared to the categorical (diagnostic) approach are: 1) evidence of continuity between clusters, 2) binary encoding of continuous data loses information, 3) correlations among diagnoses are the norm, 4) a given person may not quality for any particular diagnosis, yet have severe symptoms.
Some aspects of mental problems haven't been integrated into the hierarchical model yet. Say, unusual sexual interests (from foot fetishes to pedophilia and rape fetishes).