For a time I thought p-values were an objective measure but then a couple of blows put to rest my dream on having an objective procedure to deal with uncertainty. This is the story of the Subjectivity one-two combo that knocked out down flat my Objectivity dreams…
Saint 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:
- Win at online dating: Show yourself the way your are.
- Pick the perfect partner: Choose whoever is Continue reading
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.
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.
- Grand Challenges (datascienceatl.wordpress.com)
- Bad Science vs. Good Science: A Guide for the Layperson (Part 1) (theonlinephotographer.typepad.com)
- Not breaking news: many scientific studies are ultimately proved wrong! (theguardian.com)
- Become A Data Scientist … In 11 to 12 Weeks? (starbridgepartners.com)
- Get Hired as a Certified Data Scientist (cloudera.com)
- Are We Marginalizing Science, or Is Science Marginalizing the Humanities? (newrepublic.com)
- You Don’t Need a PhD to do Data Science (datascience101.wordpress.com)
- Data Science: What’s The Half-Life Of A Buzzword? – Forbes (semanticreatures.com)
- What is a data scientist? (tsafavi.wordpress.com)
- Command-line tools for data science (jeroenjanssens.com)
I find the use of statistics in the justice system a thrilling subject, specially so when you find out that some persons like Lucia de Berk have been handed life sentences based solely on flaw statistics coming from experts like Mr. Henk Elffers. So I’ll talk in this post about what he did wrong and how to avoid this kind of huge boo-boo in our statistical lives.
The use of statistics in the justice system has actually a long history, the amazing mathematician / engineer / physicist / philosopher of science Henri Poincaré already had to correct the misuse of statistics in the infamous Dreyfus trial.
But it was in the Lucia de Berk trial where combining p-values wrongly handed her a life sentence. I won’t go into the details of the trial, for that there are many other places like Mr. Richard D. Gill web page account of the trial and a video worth to have a look to. Instead I will focus on how to appropriately deal with a bunch of p-values to make sense of our data. Continue reading
I thought about making a post about the whole Bayesian community overreaction over CERN using p-values to announce that a Higgs like particle was discovered, but since I cannot possibly explain it better than Ms. Mayo‘s great blog in this post an others (specially if you like pink color), what I am going to do is to explain it different…
Warning for Bayesians: watching this video without sense of humor may cause difficulty breathing, swelling of your face, lips, tongue, or throat; chest pain; sweating and irregular heartbeats… Yes, just like Viagra, but just like Viagra it is worth it.
This video actually contains more reality that the sarcasm clothing might lead you to believe, so I’ll let you enjoy the game of figuring out what is real and what is not. 😉
I am tempted to make more posts about this so highly interesting and fun Bayesian vs Frequentist philosophical issue, but for now suffice to say that I basically agree with Mr. Bradley Efron‘s opinion in his science magazine article about the subject where he says:
My own practice is to use Bayesian analysis in the presence of genuine prior information; to use empirical Bayes methods in the parallel cases situation; and otherwise to be cautious when invoking uninformative priors. In the last case, Bayesians calculations cannot be uncritically accepted and should be checked by others methods, which usually means frequentistically.
One would think that humanity would not have a need for good random number generators until computers and simulations were invented since, for most practical purposes, tossing a coin or throwing a die should suffice us all. So you can imagine my surprise when I saw in this four to five thousand years old Chinese divination book called I Ching a RNG algorithm that reminds modern Linear Congruential Generators! But why the need for such a complex procedure to render random numbers?
The I Ching divination process requires to randomly select two trigrams via a rather convoluted process using either stems of Artemisia or Yarrow. And although I acquired this ancestral book a long, long, time ago, truth is that when reading it as an oracle I always used the simplified version for
lazy busy people consisting in simply tossing three coins and checking the combination of heads and tails.
I always thought that the traditional form was just a magical way to do the same thing that we can do by tossing three coins, but today, for no particular reason that having too much free time in my hands, I gave a deeper mathematical look to this traditional form and it turns out that it renders a complete different random result that tossing three coins!
Well, a mathematical curiosity you might think, but does it matter? It might! Millions of people seek advice using the simplified coin version to render the I Ching Yin Yang oracles. In this post I will show how the three coins method yields an equal proportion on Old Yin and Old Yang oracles signs whereas the traditional method yields three times more Old Yang signs than Old Yin!
This means that The I Ching, in its traditional form to draw oracles, promotes Yang behaviour over Yin, that is, it promotes among its users action, imagination, creativity, strength whereas, nowadays, with the simplified three coin version, the active and passive answers are even out.
I am not a sinologist nor a psychologist so I cannot really tell what version would have a better influence among practitioners lives, but I know though that the traditional form promotes Yang among those seeking advice which, at first glance, seems like a positive thing to do and, since this book is used by millions of people, maybe experts in the field should advice to practitioners not to use three coins anymore when using the I Ching. For those interested in having a traditionally sound oracle in terms of probability, I will show a few simple ways to achieve just that at the end of this post.
This book has impressed mathematicians like Leibniz, psychologists like Jung, poets like Jorge Luis Borges and all kind of intellectuals all over the world for centuries. And regardless you believe or not whether it has magical properties, what is certain is that it has deep psychological sapiential ones. This is not only the oldest book in human history, but a beautiful one. So, before we plunge into the mathematical details of the traditional algorithm to draw oracles, let’s share this poem from Borges about the I Ching to break the ice.
|For a Version of I Ching||Para una versión del I King|
|The future is as immutable
As rigid yesterday. There is nothing
That is no more than a single, silent letter
In the eternal and inscrutable
Writing whose book is time. He who walks away
From home has already come back.
Our life Is a future and well-traveled track.
Nothing dismisses us. Nothing leaves us.
Do not give up. The prison is dark,
Its fabric is made of incessant iron,
But in some corner of your cell
You might discover a mistake, a cleft.
The path is fatal as an arrow
But God is in the rifts, waiting.
El porvenir es tan irrevocable