# a brief on naked statistics

April 2, 2013
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“This book will not make you a statistical expert (…) This book is not a textbook.” (p.xv)

With this warning in mind about my bias, let’s get on with what’s in this book. The above tells us what isn’t. To quote further from the author, the book has been designed to introduce the statistical concepts with the most relevance to everyday life (p.xv). Naked Statistics goes over the basic notions of statistics (mean, standard deviation, correlation, linear regression, testing, design, polling), gives a sprinkle of probability background (counting models and the central limit theorem, which Wheelan considers as part of statistics), and spend the remaining chapters warning the reader(s) about the possible missuses of models and statistical tools if implemented in the wrong situations or with the wrong type of data. (There are a few graphs, but they are not particularly inspiring.) All this done with the minimum amount of maths formulae, mostly hidden in footnotes and appendices. (But then why adding an extra formula for σ when one is given just before for σ²?!) Sometimes, the minimum is not enough, as demonstrated by the “formula for calculating the correlation coefficient” (p.61) which takes a whole page of text to get around this absurdity of not using maths symbols like Σ and concludes with the lame “I’ll wave my hands and let the computer do the work” (p.61)! Somehow surprisingly, given the low-key nature of the book, it includes a final appendix on statistical software. From Excel, to SAS, Stata, and …R! While I am pleased at this inclusion, it sounds very much orthogonal to the purpose and the intended audience of Naked Statistics. I cannot fathom anyone reading the book and then immediately embarking upon writing an R code without stopping by a statistics textbook or formal training. (Incidentally, the author reproduces the usual confusion between free and open source, p.259.)

Lest you hurl the book across the room again, I have put the formula in an appendix.” (p.159)

In the chapter about possible misuses of probability models (and statistics), Naked Statistics predictably takes the example of the “most irresponsible use of statistics”, namely the role of inappropriate VAR models in the 2008 crisis. Somehow inevitably, Nassim Taleb’s The Black Swan: The Impact of the Highly Improbable slides in to dispense its widsom. (As an aside, in this chapter, I tried to make sense of the mindboggling sentence “My mother has had three holes in one” (p.99) and could not. Until I found on Google it was a golfing expression…) While the book abounds in reasonable examples on the misuse of statistics, I am not convinced this is the most relevant one, esp. because those unrealistic models were so very rarely based on any data.

Regression analysis is the hydrogen bomb of the statistics arsenal.” (p.213)

One chapter that proved useful to me was Chapter 5: “Don’t buy the extended warranty on your \$99 printer”. Indeed, on the same afternoon I read Naked Statistics, I went to buy an electrical appliance and could bring the book as an argument to refuse this extra-warranty the seller was really eager to let me “benefit from”, for a mere 1/6th of the cost..! (I also liked the trick of Schiltz beer to apparently induce [some] Bud drinkers into switching to this competitor brand.) On the other hand, while I could not spot any statistical blunder from my breakfast cursory read, I did not agree with Wheelan calling “Delma Kinney, a fifty-year-old Atlanta man [who] won \$1 million in an instant lottery game in 2008 and another \$1 million in an instant lottery game in 2011″ (p.9) a “statistical anomaly”. The probability of “q in 25 trillion” advanced right after is a typical example of the wrong type of conditioning: as explained in several posts on the ‘Og, such occurrences are bound to happen, sometime, somewhere, if most likely not to Mr Delma Kinney!

The resulting performances will be closer to the mean.” (p.106)

In conclusion, while I do not see much specific appeal in Naked Statistics, I reckon this is one of many books pointing out the possible misuses of statistics to the general public and bringing some awareness as how to re-analyse and debunk (with the proper amount of training) statistics found on the news. In that respect, it does not differ so much (in spirit) from How to lie with Statistics, even though the current book has more current real-life examples. That may be used in the classroom if needed.

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