IQ Tails of Race & Gender

ouroboros042Fear not, I am not going to perform any analysis proving the superiority of any race or gender. Also, as a 100% Spaniard (I need to check on that though) I do not belong to the “elite” of ethnics groups disputing supremacy, namely: Northern Europeans, Jews and Far-East Asians and, quite frankly, I feel kinda good about it since I’d rather stick to the Latin Lover stereotype which, by all means, it is true.

Nonetheless, in a recent tweet by Julian Assange in which he shows how Google applies censorship on certain topics (an interesting discussion for another moment), Mr. Assange linked to a video titled Steven Pinker – Jews, Genes and Intelligence.

I would have disregarded the video as your standard white supremacy internet rhetoric except for I know Steven Pinker from his published works and achievements, and he is no small fish in the Psychology and Cognitive Science world. That is why I decided to give a shot to his video to see what’s what until he began talking about statistics. These are his words:

“…Jewish achievements might have an explanation on another fact that has long been known; that Jewish score on average higher on IQ tests than any ethnic group for what there’s comparable data. Their mean IQ is between 108 and 115, the mean of the European population is by definition a hundred which means that the Jewish average is a whole standard deviation higher than the [European] average… Importantly, even if the effect is moderate on average it’s a mathematical fact in Normal Distributions, that is Bell’s Curves, that small effects in the average can translate into huge effects at the extreme… So with one standard deviation difference between groups a score that is three standard deviation above the mean in the higher distribution is four standard deviations in the lower distribution which means there are 42 times as many people at that cut off.” – Steven Pinker

In short, according to Steven Pinker there are 42 times more chances for a Jewish baby to be born an IQ genius than for an European one… But, is that really so?


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If you play with your Prior you’ll go blind


And thus, the Huffington Post predicted a 98% probability for Hillary Clinton to be the next President of the United States. Amen… Let’s tease them a little bit, shall we?

My Bayesian friends, I understand playing with your priors is a very joyful activity but you see, it leads to blindness. It allows you to believe, let me cap & bold this one, BELIEVE that Hillary’s chances to be the next President of United States were 98%! No wonder that betting sites favored heavily Hillary’s side days before the election! I mean 98%! Who wouldn’t put some money there. Right?

But you know, a 98% probability coming from a Bayesian means very little unless, of course, they do some math pirouette to guarantee that the probability has frequentist properties, but then, if they do that, why bother going Bayesian in the first place?

If a frequentist tells you there is 98% probability for an event to happen he/she means that 98 out of 100 times where you find yourself in a situation like where the event is taking place the event will occur. Now, if a Bayesian tells you there is 98% probability he/she means that this is his/her degree of believe (wot?) on the event to happen… Amen again.

In other words, Bayesian results are as credible as the beliefs of the Bayesian statistician making the calculations, now we can understand why they calculate credible intervals instead confidence ones.

If we check on the Huffpo methodology we can read:

Many Bayesian models ― including the Pollster averaging model as it’s implemented for our charts ― use “uninformed” priors that don’t affect the model or provide any background information.

However, we do use information from previous elections in these priors to make predictions in our presidential model.

Ba dum tsssss

Much has been written on the pros and cons of going Bayesian and how evil Frequentists are, but this amazing Bayesian result from Huffpo was just too good to let go as a beautiful example of how blind you can go when playing with your priors.

Social Network Analysis & GOP Verbal Attacks

Not that I know anything about the GOP debates or candidates, but I casually saw in a CNN post this nice visualization of verbal attacks during the RL GOP Debate, and I thought that I would do a little SNA and try to draw conclusions on the debate WITHOUT actually having seen it…

let’s see how it goes and, please, if you’ve seen the debate and know better than me, let me know if I am very wrong 🙂

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Media on “Video Games & Violence”
                                           Click to watch video

This is the numerical result the researchers used to make their “ridiculous” assumptions in their paper:

The ANOVA procedure for repeated measurement designs yield significant results for the

  • dACC (Wilk’s Λ = 0.33, F = 4.59, p <.027, η2 = 0.67)
  • rACC (Wilk’s Λ= 0.19, F = 9.55, p <.003, η2 = 0.81)
  • amygdala (Wilk’s Λ = 0.28, F = 5.75, p<.014, η2 = 0.72)

Tests for linear trends were significant in the three ROIs:

  • dACC: F = 8.28, p < .014;
  • rACC: F = 17.97, p < .001;
  • amygdala: F = 30.02, p < .001

but not for higher order trends

The other study they mention does not involve any experiment and is merely a review of other studies.

Whether the significance in the study is significant for science is up to the researchers but, yeah, we can make assumptions with a sample size of just 13. Interestingly, others would regard “too many” people in a sample size as a manipulation to achieve significance. So I guess that when we don’t like something we can always find reasons to complain about it.