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3.6 out of 5 stars14
3.6 out of 5 stars
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on 27 October 2007
The concept was reasonable: to show how statistical analysis was changing the way in which the businesses and governments operated. The author also set out the relevant topics sensibly; chapters covered such issues as why this is occurring now, random samples, the reaction of experts to such analysis (with an extra focus on the medical profession) and the legal/ moral issues that this type of analysis raises. The trouble with this book is that the writing style is too simplistic. It's not obvious whether this is the fault of the editor or the author or a combination of the two. Various examples of data analysis are used without it being clear what point the author is trying to make. The result is that at the end of many chapters it's unclear what the author's intent was. There are two other aspects of the writing which are irritating. First, every person whose work is described in the book is given a brief biography which reads as if they were some form of matinee idol. Secondly, the author uses the opportunity to air some personal professional disputes which for anyone outside the argument is tedious. Overall, it's a book you may buy at an airport to read on a flight and throw away
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on 16 October 2007
What this book does well is to show how valuable numerical information can be if you know how to analyse it properly, and how unreliable human judgment can be compared to what the data can tell us. The examples are well chosen and telling. Where it goes too far is in expressing almost unqualified belief that the number stuff trumps everything else, all the time. Contrary to the title, not everything can be predicted. Specifically, the sudden turns of events, in markets or social behaviour (which are often what we are most interested in) are not predictable on the basis of past patterns. By defintion, surprises are not predictable. And there always has to be a human doing the modelling, working out the dynamics of any set of causal relationships. Sometimes, these people get it wrong. Then it's the old case of "rubbish in, rubbish out." But though he has pushed the argument too far, he is interesting, imaginative and persuasive that the balance could be tipped a little further than it now is, towards making better use of statistical data.
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I came across a reference to this book in a news article about Epagogix, a company that analyses some of the characteristics of film scripts in an attempt to identify whether a film will be successful or not. The author uses Epagogix as an example of the way in which simple statistical models are increasingly being applied to large data sets in order to improve decision making in a range of business and social applications. The interesting point is that the models often outperform the judgement of human experts in tasks such as pricing airline seats, diagnosing and treating disease, finding a life partner and paroling prisoners.

One of the other example tasks he cites is the choice of book titles, and demonstrates it with an account of how he used the statistical technique of randomized testing with Google AdWords to choose between "The End of Intuition" and "Super Crunchers", based on the number of clicks each received. This - perhaps inadvertently - isn't really a good advert for the technique, since the former title actually means something, while the one he ended up using doesn't (what's the difference between a super cruncher and a regular, standard, non-super cruncher anyway?).

However, its use is in keeping with the author's breezy, non-technical style (e.g. "He's the kind of guy that would quickly disabuse you of the notion that number crunchers are meek, retiring souls. I've seen [him] stride around a classroom, gutting the reasoning behind a seminar paper with affable exuberance" [p2]) that tries to spice up what could have been a dull subject in other hands. Thus, he describes the techniques - regression analysis and randomized testing - briskly without any mathematics (there are only a couple of diagrams in the entire book) before moving onto stories about their application. Most of the technical detail and justification has been moved to a notes section that takes up more than a tenth of the book.

I enjoyed reading this account, particularly his exploration of why this sort of thing is becoming more widespread (increasing computer power, cheaper storage, wider accessibility via the internet) and its implications for privacy and control. Those who found Freakonomics stimulating will, I think, like this as well (it should be noted that the present author has worked with one of that book's coauthors - in the past), since it provides some intriguing insights into the way in which trends in the world and our behaviour in it can be unraveled and predicted.
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on 3 March 2008
"There are three kinds of lies," said Benjamin Disraeli, "lies, damned lies and statistics." But, like it or not, the world is becoming more quantitative every day and no one can afford to be statistically innumerate. If you live in Excel and use quantitative techniques daily, this may come as no surprise. What may be surprising, even to data-heads, is the extent to which statistical methods are illuminating areas of human life hitherto relegated to "experts." Call it the new age of empiricism or the rise of numerical "super crunchers," but, whatever the name, the trend is real. In this book, Yale law professor and econometrician Ian Ayres provides an unbiased sample of entertaining anecdotes showing how quantitative thinkers are taking over and why the trend is unlikely to abate. The caveat: as the world and its feedback loops get increasingly complex, is regression less useful? If so, Ayers is a bit optimistic. Yet, getAbstract finds that his book, as well as being entertaining and vigorously written, offers a painless review of important statistical ideas that even Disraeli would've found hard to challenge.
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on 29 December 2008
"Super Crunchers" is a breezy and somewhat glib account of how quantitative methods are replacing traditional expert judgement. The author, Ian Ayres, is a law professor at Yale University that often has used quantitative methods in his research, some of which has been conducted in co-operation with Steven D. Levitt of "Freakonomics: A Rogue Economist Explores the Hidden Side of Everything" fame.

In this age of plenty when it comes to computing power and information, traditional quantitative methods for decision-making such as regressions and randomised trials are spreading to new fields. A regression on meteorological data performed by Orley Ashenfelter is a better predictor of the eventual price of Bordeaux vintages that the expert judgement of Robert Parker. In the USA government is using randomised trials to judge the efficacy of government programs. Ayres also mention methods such as neural networks that can be used to find patterns in data.

The advent of "super crunching" has its downsides. It will automate decision-making, reducing the scope for judgement in many white collar jobs. Loan officers at banks these day do little more than feed the computer with information. There is a movement for evidence-based medicine where doctors are aided by computers in the diagnoses of patients. Even jobs such as teaching could turn out to be less interesting in the future. In the USA some schools have adopted Direct Instruction, where the entire lesson is scripted, the script having been developed and tested using quantitative methods.

In the beginning of the book Ayres seems quite unconcerned about privacy, but this is dealt with in a later stage of the book. What he never really handles are the limitations of the methods he touts. Statistics is a hard tool to master, witness the many mistakes in scientific papers. Data-snooping biases is a real danger in data mining, when testing a huge number of hypotheses some are bound to pass conventional tests for statistical significance by pure chance. A hilarious riposte to evidence-based medicine was an article in the BMJ where they found that there were not enough data to support the hypotheses that parachutes are effective in preventing major trauma related to gravitational challenge.

Despite this the book is well worth a read. It is funny and interesting. If you are interested in super crunching methods in a business setting, you might want to read "Competing on Analytics: The New Science of Winning" by Thomas H. Davenport and Jeanne G. Harris.
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on 21 February 2009
This read must have been compelling a couple of years ago. But the 'super crunching' this book advocates - didn't do a good job in predicting the credit crunch. It probably played a role in creating it. The book makes case after case against human intuition - which may work in many instances. Though an updated version of the book - highlighting the role of statistical models in banks lending to sub-prime, credit cards being given to people who can't repay, its failure to predict the decline in home prices - this would allow us to really evaluate the strengths and potential pitfalls of so called Super Crunchers.
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on 7 April 2009
Although this book is not for everyone, those with an interest in the commercial application of what can be done (and what is already being done) with vast amounts of data, will find it brilliant. A far cry from the usual academic stuff, this isn't a conspiracy theory book either. It is full of entertaining and genuinely interesting real-life examples; this is a very accessible book. If in doubt, definitely buy.
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on 7 September 2007
Tiresome. One lengthy anecdote after the other -- albeit, some of interest. No conceptual depth. Surprising that such work can come from an associate of Yale University. However, just as the author "supercrunched" the choice of the title, he may have used the same approach in order to determine what may best sell to a mass of undiscerning readers.
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on 28 February 2013
I'm far from an expert in this field so can't make any comment on whether the contents of the book are accurate, but I found it very engaging to read, enjoyable, and certainly got me thinking about the implications of number crunching to evaluate the world around me. Recommended to me by a friend, and I'd strongly pass that recommendation on.
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on 30 December 2010
If you know some basic statistics there is not much new here in terms of new methods. What makes the book interesting is the way the internet and the world in general produces large amounts of data about all sorts of things, now making it possible to analyse, predict and manage all sorts of things. Quite inspirational.
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