numbersense (book review)

While I got an advance reader’s copy of numbersense, Kaiser Fung’s latest book, sent to me by the publisher McGraw-Hill, I did not managed to write a review until the book had been out for two months. The title of the book is clear enough about the purpose of the author, but the subtitle “How to use Big Data to your advantage” stresses it even further. And includes the sesame “Big Data”, much more likely to appeal to the general reader than “statistics”…!

“I wouldn’t blame you if you are ready to burn this book, and vow never to talk to the lying statisticians ever again.” (p.4)

So why did it take me such a long while to compose this review?! Besides the break induced by The Accident (I took the book to the hospital but ended up reviewing R for Dummies instead!), I figure I got rather taken aback by the style and intended audience of numbersense, given my earlier reading and enjoying Numbers rule your world. While the book remains of interest for statisticians (and other CHANCE readers!), providing examples to use in the classroom, the statistical connection is all but visible to the casual reader who may well conclude that numbersense is a form of numerical common sense of about fighting innumeracy, rather than modelling uncertainty thru statistical models.

“In analyzing data, there is no way to avoid having theoretical assumptions (…) The world has never run out of theoreticians; in the era of Big Data, the bar of evidence is reset lower, making it tougher to tell right from wrong.” (p.11)

Overall, the intended audience of numbersense seems even further away from statistically savy readers than Numbers rule your world. The book is divided into four sections: social data (Chap. 1 & 2), marketing data (Chap. 3-5), economic data (Chap. 6 & 7), and sport data (Chap. 8). Plus a prologue on the Simpson paradox (in marketing), involving Howard Wainer whose Uneducated Guesses: Using Evidence to Uncover Misguided Education Policies I reviewed a while ago. The first (more marketing than social) chapter is about doctoring admission policies against GPA and LSAT scores (whatever that means!) to improve the ranking of a school. This does not sound such a major numerical issue (once the trick is uncovered) and the chapter meanders too much to my taste. The second chapter goes back to Quetelet‘s impossible average man. Asking the reader to question the role of indices in definitions (like obesity). And mentioning the “significant result” bias in medical journals in passing. As well as causality. As in the previous chapter, I finished it waiting for a conclusion that never came. Chapters 3 and 4 focus on Groupon, Without much of a statistical model (except maybe a second-order Simpson paradox?). Chapter 6 is about how companies like Amazon target their suggestions to customers. Not elaborating on the logit or whatever model is behind, though, and drifting aside on the breach of data secrecy by most of “those” companies.  The economics chapters are more to my liking, presumably because they are more standard, covering the subtleties of unemployment and inflation (official) statistics. They fall into what I call the Gini index branch of statistics. At last, the sport chapter is about fantasy football (FF) and not about Moneyball (even though it has links, obviously). I did not go father than a quick perusal at the chapter as I did not understand (most of) the point of the chapter (or of playing FF). For instance, the conclusion seemed quite distanced from the actual story…

“today’s computers do not understand languages. All they  do is match text: they can tell me whether the words “empirical Bayes model” are found on a specific Web page.” (p. 209)

The epilogue is of a different nature as it describes two examples of the tasks undertaken by Kaiser Fung as a data analyst. A nasty data transfer. And a manual classification of some Google queries. This may be the part of numbersense that I enjoyed the most. Again, let me stress I have no scientific complaint about the book: it just sounds too low-tech’ for my taste. And I find it is not helping readers to go beyond the first level of scepticism about raw and processed data. Because they are not data-analysts.

Uneducated guesses

I received this book, Uneducated Guesses: Using Evidence to Uncover Misguided Education Policies by Howard Wainer, from Princeton University Press for review in CHANCE. Alas, I am presumably one of the least likely adequate reviewers for the book in that

• having done all of my academic training in France (except for my most useful post-doctoral training in Purdue and in Cornell), I never took any of those ACT/SAT/&tc tests (except for the GRE at the very end of my Ph.D. towards a post-doctoral grant I did not get!);
• teaching in a French university, I never used any of those tests to compare undergraduate or graduates applicants;
• I am very marginally aware of the hiring process in US universities at the undergraduate, even though I knew about the early admission policy;
• there is no equivalent in the French high school system, given that high school students have to undergo a national week-long exam, le baccalauréat, to enter higher education and that most curricula actually decide on the basis of the high school record, prior to [but conditional on] the baccalauréat.

Thus, this review of Wainer’s Uneducated Guesses is to be taken with pinches (or even tablespoons) of salt. And to be opposed to other reviews. Esp. in Statistics journals (I could not find any).

“My role in this parallels Spock’s when he explained `Nowhere am I so desperately needed as among a shipload of illogical humans.‘” (page 157)

First, the book is very pleasant to read, with a witty and whimsical way of pushing strong (and well-argued) opinions. Even as a complete bystander, I found the arguments advanced for keeping SAT as the preferential tool for student selection quite engaging, as were the later ones against teacher and college rankings equally making sense. So the book should appeal to a large chunk of the public, as prospective students, parents, high school teachers or college selection committees. (Scholars on entrance tests may already have seen the arguments since most of the chapter are based on earlier papers of  Howard Wainer.) Second, and this is yet another reason why I feel remote from the topic, the statistical part of the analysis is simply not covered in the book. There are tables and there are graphs, there are regressions and there are interpolation curves, there is a box-plot and there are normal densities, but I am missing a statistical model that would push us further than the common sense that permeates the whole book. After reading the book, my thirst about the modelling of education tests and ranking is thus far from being quenched! (Note I am not saying the author is ignorant of such matters, since he published in psychometrics, educational statistics and other statistics journals, and taught Statistics at Wharton. The technical side of the argument does exist, but it is not included in the book. The author refers to Gelman et al., 1995, and to the fruitful Bayesian approach on page 69.)