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?

jews_vs_europeans.png

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Data Science vs Bimbo Math

Ms. FrySaint Valentine, that romantic and beautiful festivity for department stores also brings everybody to talk about love in all sort of contexts and TED, my favorite talk place (I will have to rethink about this), brought for the occasion complexity theorist Hannah Fry to talk about The Mathematics of Love. She summoned the almighty and powerful daemon of Mathematics in a quite entertaining talk to reveal us all mere mortals the secrets of Love… Not really.

So many things to tell about this talk I do not know where to begin. But you know what, TED picking a math bimbo to sell books; I can understand. Turning Science into show business to make it appealing to the general public; I am for it.  Oversimplifing complex subjects to make them accessible to everyone even if the oversimplification is not quite true; I can take that. Using all the previous to push people into taking life changing decisions based on sloppy science… Well, allow me to draw a line there Ms. Fry. Science is acquiring a bad reputation little by little and talks like these are one of the reasons why.

Anyway, long story short, ignore her love tips and specially #2, that one is really damaging. On my side, I will use Data Science and common sense to show that the best you can do is to marry / partner the person you are in love with when you are in love. And when it comes to use reason in the field of love, allow me please to quote Monsieur Blaise Pascal on this one:

“The heart has its reasons of which reason knows nothing”

Let’s now kick some ass in the name of good science. Misses Fry present us with three “Mathematically Verifiable” tips to:

  1. Win at online dating: Show yourself the way your are.
  2. Pick the perfect partner: Choose whoever is Continue reading

Scientist at last, Scientist at last, thanks God almighty I’m a Scientist at last!

I wanted to be a scientist ever since I read a comic where scientist Bruce Banner turns into The Incredible Hulk. I did not know what a scientist was or what kind of scientist I wanted to be, yet, I thought that the scientific career sounded like lots of fun if it can turn you up into a huge green monster.

bruce banner
How Scientists look like for a 12 years old

I guess that for children of my age back in those days Marvel comics were the closest thing to Harry Potter for children nowadays (Let’s get ready for a massive turn up of sorcerers and witches in the coming years by the way).

So there I am after a few years since I read the comic and for reasons beyond this post but that can easily be described like a billiard break(ing bad) I end up with a couple of degrees; Computer Science and Statistics, and a Master in Operation Research (more of the same stuff).

Yet, I never considered myself (nor did anyone else) as a scientist or a researcher since, well… when programming I don’t feel much like doing science no matter how big the word science is in my Computer Science degree, and the degree in Statistics does not make me feel like an scientist either nor the Master like a researcher.

Statistics by themselves are just a field of mathematics and mathematicians are more into precise grammar than into writing beautiful books. Not to mention the opinion of physicists like Feynman about the current use of statistics for science that downgrades Social Sciences and other fields into Pseudo-Science.

There was a time when some of the work I was doing could be named as Data Mining and this seemed to push me further and further away from my childhood dream since now I could be considered a Miner instead of a Scientist… Don’t get me wrong, Miner is no a bad profession if you want to start a revolution but all the glamour of the word science was gone and so my dream to be a scientist darkened with soot.

But then… Data Science came along, wait, what? That’s right! Data Science is what you get when we consider every procedure that brings us knowledge stripped of any field background, the intersection of every science known to men, the Mixed Martial Arts of knowledge. Data Science… if you think about it, can there be any other kind of science?

Not surprisingly when meeting with fellow Data Scientists we’ll find out they come from all sort of venues and that data science teams are usually Macedonian salads of scientific backgrounds which include Physicists (of course) and Musicians (you heard me).

So turns out that after so many years my dream came true and I became exactly what I wished back in those days: a Scientist with no particular field… but data, and since everything is data, now everything is my field. So I can finally proudly say “Scientist at last, Scientist at last, thanks God almighty I’m a Scientist at last!”.

And now if you excuse me I have to go back to my scientific project codenamed Green. Thank you very much.

15 to 42 percent of medical research are false positives (Yet Another Calculation)

A while ago I found a very interesting paper from Leah R. Jager and Jeffrey T. Leek  via a post in the Simply Statistics blog arguing that most published medical research is true with a rate of false positives among reported results of 14% ± 1%.  Their paper came as a response to an essay from John P. A. Ioannidis and several others authors claiming that most published research findings are false.

After dealing with some criticisms Mr. Leek made a good point in his post:

“I also hope that by introducing a new estimator of the science-wise fdr we inspire more methodological development and that philosophical criticisms won’t prevent people from looking at the data in new ways.”

And thus, following this advice, I didn’t let criticisms prevent me from looking at the data in a new way. So for this problem I have devised a probability distribution for p-values to then fit the data via MLE and infer from there the rate of false positives.

pvalues PDF CDFSo this is my take; 15.33% rate of false positive with a worse case scenario of 41.75% depending on how mischievous researchers are but, in any case, and contrary to what others authors claim, most medical research seems to be true.

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Climategate (3/3): Fear mongering

The last post in this Climategate series is dedicated to the climate of fear mongering we all see every now and then in the media claiming extreme weather patterns linked to global warming in an end-of-the-world tone. I will offer some insights and calculations to show that “extremist” might be wrong.

So let us begin with the Australia Climate Change? New Colors Added To Forecast Maps news that spread like… well.. wildfire not just in Australia but through all over the world.

o-AUSTRALIA-CLIMATE-CHANGE-570

It seems that poor climatologists in Australia had no choice but to reuse the purple color already in use for the negative range (-25, -18) ºC for the positive range (50, 54) ºC. What could possibly have done these people but to mix cold and hot weather colors!? well, here’s an idea:

Not Scary Colors for Australian Temperatures

There it goes a present for climatologists in Australia; 121 not scary-oh-my-gaw-how-hot-it-is different colors for the range (-60,60) ºC, and just in case you need more I have a few spare millions. You’re welcome.

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Climategate (2/3): be careful what you model for because you might get it

In the previous post I showed how James Hansen at GISS NASA clearly over estimated global warming in the late 80’s due to the modeling choices he made. To make a point on how influential the choice of a model is, in this post I will make modeling choices that will allow us to claim that global warming can be explained as a fluke in a random process.

I like to explain the relationship between data and models saying that data is the shadow reality casts, and models are what we believe is casting the shadow. So once we have a model  we can use it to cast shadows (make predictions) like the one James Hansen did and could be read in 1986 newspapers:

Hansen predicted global temperatures should be nearly 2 degrees higher in 20 year. “Which is about the warmest the earth has been in the las 100,000 years.”

Interestingly James Hansen downgraded his prediction in a 1988 paper from the nearly two degrees higher to a one degree higher. Though to be fair I would not be surprised if media misquoted him; I might not trust scientists but I absolutely distrust media.

Anyhow, let’s now compare NASA’s prediction in this 1988 paper (in red) to what actually happened years later (in blue):

Yearly Average Global Temperature Change
1988 NASA predictions on top of yearly average global temperature changes with major volcano activity and parts per million levels of CO2

Data for this plot comes from the B.E.S.T and N.O.A.A.

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Climategate (1/3): be careful what you model for because you might get it

Deception is all around us, in every little parcel of our life; from our personal Bart Simpson’s “It wasn’t me” to our local TV news host selling us the latest “You’re not going to believe this” but we eventually do. One might just wish there would exist communities out there with higher standards like, for example, Christians priests but, nope, they cover up pedophile networks in order to preserve The Church’s “good” name. But how about the atheist priests a.k.a scientists? How about their standards?

Well, unfortunately the community of scientists might have more to do with priesthood than one might expect or desire, and a nice example of this would be the Climategate (or the Climatic Research Unit email controversy, as some people had the kindness to rename the Climategate article in the Wikipedia following Fox News’ motto “fair and balanced” )

So in this post I am going to replicate earlier studies on global warming to uncover how over pessimistic were the maths models of the past, but I will also talk about human weakness, and scientists are human… for now.

Mike's Trick
Click to watch Dr. Richard A. Muller (Professor of Physics at the University of California at Berkeley) talk about “Mike’s Trick” and the Climategate.

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