‘Everybody lies’ by Seth Stephens-Davidowitz
If you are into data science, computational social science, social computing, big data or any other of the fancy terms for the power of data science applications on the large datasets we constantly obtain nowadays — then you will likely, as me, find interest in this book.
Seth obtained his PhD in economics. His ideas and the approaches to big data are so novel and out-of-the-box, at least from a perspective of a non-economist like me, that it is not a surprise to me that he has also held positions at Harvard, Google and New York Times.
Summary of the findings in the book:
Are Freudian slips real?
A simple answer is NO. The frequency with which people are found to make mistakes of the type fuckiest instead of funniest and a penestrian instead of a pedestrian while they type online turns out to be no larger than the frequency with which randomized bots would make such mistakes.
However, on another note, Freud might have been right… The Oedipus complex.
Is incest present in sexual phantasies?
In 9-16% (for women vs. men) of all the porn sites searches, such phantasies are present.
Another apparently random question explored is about successful basketball players. Is it really true that they are more likely to come from difficult family and neighborhood backgrounds?
Again the answer is NO. On the opposite, kids from mid or well-off families have higher chances of succeeding in basketball.
This question is my favorite and has nothing to do with digital big data as we come to think of them. What makes great racing horses?
Many have collected diverse data about racing horses for many years, including the parents, how they behave at certain times and about their different physical attributes. However, one man came to an idea to measure their heart sizes, in particular, the left ventricle. This turned out to be the single best predictor of most successful racing horses. Thanks to such finding he has convinced the owner of a later champion horse, the American Paharon, not to sell it away at a point when he intended to.