Super Crunchers

Ian Ayers is a surprisingly engaging writer, taking what many would consider a very dry topic — statistics — and turning it into a thought-provoking, but flawed, book entitled Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart.

From the opening pages, Ayers pits the “super crunchers” — people applying statistics to large data sets — against experts in an area, be it viticulture, baseball, or marketing. With barely suppressed glee he describes how number crunching out-predicts the experts time and time again. The point being that as collecting, storing and analysing large amounts of data becomes cheaper and cheaper, more and more decision-making will take the results of “super crunching” into account, with experts either having to step aside or learn some statistical chops. To back arguments for the rise of “super crunching” Ayers draws on a large number of examples from a variety of areas and even experiments with the technique himself, describing how he used it to help choose the title of his book.

Although I am more or less convinced by Ayers’ arguments I found myself questioning his credibility in several places during the book. I think the main reason for this was due to the tone of the book occasionally crossing the fine line separating “enthusiastic, popular account” and “overly simplistic, gushing rave”. The constant use of “super crunching” throughout the book got on my nerves after a while. It began to overemphasise the newness of what could as easily be called “statistical analysis”. After a while I mentally replaced “super crunching” with the less sensational “statistical analysis” wherever I encountered it.

Conversely, Ayers constantly refers to “regression” when talking about the techniques analysts use to make predictions. At first, I thought this was a convenient short-hand for a range of techniques that he didn’t want to spend time distinguishing between. It was only when neural networks are described as “a newfangled competitor to the tried-and-true regression formula” and “an important contributor to the Super Crunching revolution” that I realised that Ayers may not know as much about the nuts and bolts of computational statistics as I first thought. This impression was confirmed when Ayers later confuses “summary statistics” for “sufficient statistics” and talks tautologically of “binary bytes”.

Stylistically, there is too much foreshadowing and repetition of topics throughout the book for my liking. This feels a little condescending at times, as does him directly asking the reader to stop and think about a concept or problem at various points.

Overall, I wanted to like this book more than I did. It was a light, enjoyable read and I wholeheartedly agree with Ayers’ belief in the continuing importance of statistics in decision-making and his call to improve the average person’s intuition of statistics. Unfortunately, I found much of “Super Crunchers” substituting enthusiasm for coherence, as well as impressions and anecdote for any kind of meaningful argument.

Mark Reid September 27, 2008 Canberra, Australia
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