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Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart Hardcover – August 28, 2007

4.3 4.3 out of 5 stars 382 ratings

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Why would a casino try and stop you from losing? How can a mathematical formula find your future spouse? Would you know if a statistical analysis blackballed you from a job you wanted?

Today, number crunching affects your life in ways you might never imagine. In this lively and groundbreaking new book, economist Ian Ayres shows how today's best and brightest organizations are analyzing massive databases at lightening speed to provide greater insights into human behavior. They are the Super Crunchers. From internet sites like Google and Amazon that know your tastes better than you do, to a physician's diagnosis and your child's education, to boardrooms and government agencies, this new breed of decision makers are calling the shots. And they are delivering staggeringly accurate results. How can a football coach evaluate a player without ever seeing him play? Want to know whether the price of an airline ticket will go up or down before you buy? How can a formula outpredict wine experts in determining the best vintages? Super crunchers have the answers. In this brave new world of equation versus expertise, Ayres shows us the benefits and risks, who loses and who wins, and how super crunching can be used to help, not manipulate us.

Gone are the days of solely relying on intuition to make decisions. No businessperson, consumer, or student who wants to stay ahead of the curve should make another keystroke without reading
Super Crunchers.
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Editorial Reviews

From Publishers Weekly

Yale Law School professor and econometrician Ayres argues in this lively and enjoyable book that the recent creation of huge data sets allows knowledgeable individuals to make previously impossible predictions. He calls the data set analysts super crunchers and discusses the changes they're making to industries like medical diagnostics, air travel pricing, screenwriting and online dating services. Although Ayres presents both sides of this revolution, explaining how the corporate world tries to manipulate consumer behavior and telling consumers how to fight back, his real mission is to educate readers about the basics of statistics and hypothesis testing, spending most of his time in an edifying and entertaining discussion of the use of regression and randomization trials. He frequently asks whether statistical methods are more accurate than the more intuitive conclusions drawn by experts, and consistently concludes that they are. Ayres skillfully demonstrates the importance that statistical literacy can play in our lives, especially now that technology permits it to occur on a scale never before imagined. (Sept. 4)
Copyright © Reed Business Information, a division of Reed Elsevier Inc. All rights reserved.

Review

"In the past, one could get by on intuition and experience. Times have changed. Today, the name of the game is data. Ian Ayres shows us how and why in this groundbreaking book Super Crunchers. Not only is it fun to read, it just may change the way you think."—Steven D. Levitt, author of Freakonomics

"Data-mining and statistical analysis have suddenly become cool.... Dissecting marketing, politics, and even sports, stuff this complex and important shouldn't be this much fun to read."—
Wired

"[Ayres's] thesis is provocative: Complex statistical models could be used to market products more intelligently, craft better movies, and solve health-care problems—if only we could get past our statistics phobia."—Portfolio

"When statistics conflict with expert opinion, bet on statistics....Businesses, consumers, and governments are waking up to the power of analyzing enormous tracts of information."—
Discover

"
Super Crunchers shows that data-driven decisionmaking is not just revolutionizing baseball and business; it's changing the way that education policy, health care reimbursements, even tax regulations are crafted.  Super Crunching is truly reinventing government.  Politicians love to tout policy proposals, but they rarely come back and tell you which ones succeeded and which ones failed.  Data-driven policy making forces government to ask the bottom line question of 'What works.'  That's an approach we can all support."—John Podesta, President of the Center for American Progress

"A lively and yet rigorously careful account of the use of quantitative methods for analysis and decision-making.... Both social scientists and businessmen can profit from this book, while enjoying themselves in the process."—Dr. Kenneth Arrow, Nobel Prize winning economist, and Professor Emeritus at Stanford University

“Ayres’ point is that human beings put far too much faith in their intuition and would often be better off listening to the numbers.... The best stories in the book are about Ayres and other economists he knows, whether they are studying wine, the Supreme Court or jobless benefits.... Ayres himself is one of the [statistical] detectives. He has done fascinating research.”—
The New York Times Book Review

"Ian Ayres [is] a law-and-economics guru."—
Chronicle of Higher Education

“Lively and enjoyable.... Ayres skillfully demonstrates the importance that statistical literacy can play in our lives, especially now that technology permits it to occur on a scale never before imagined.... Edifying and entertaining."—
Publishers Weekly

"Super Crunchers presents a convincing and disturbing vision of a future in which everyday decision-making is increasingly automated, and the role of human judgment restricted to providing input to formulae."—The Economist

"Insightful and delightful!" —
Forbes

Product details

  • Publisher ‏ : ‎ Bantam; 1st edition (August 28, 2007)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 272 pages
  • ISBN-10 ‏ : ‎ 0553805401
  • ISBN-13 ‏ : ‎ 978-0553805406
  • Item Weight ‏ : ‎ 1.03 pounds
  • Dimensions ‏ : ‎ 6.25 x 1 x 9.3 inches
  • Customer Reviews:
    4.3 4.3 out of 5 stars 382 ratings

About the author

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Ian Ayres
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Ian Ayres is the William K. Townsend Professor at Yale Law School and the Yale School of Management, and is editor of the Journal of Law, Economics and Organization. In addition to his best-selling SuperCrunchers, Ayres has written for the New York Times, the Wall Street Journal, Financial Times, International Herald Tribune, and The New Republic. He lives in New Haven, Connecticut.Barry Nalebuff is Professor of Economics and Management at the Yale School of Management. His books include The Art of Strategy (an update of the best-selling Thinking Strategically) and Co-opetition. He is the author of fifty scholarly articles and has been an associate editor of five academic journals. He lives in New Haven, Connecticut.

Customer reviews

4.3 out of 5 stars
382 global ratings

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Customers say

Customers find the book insightful and well-written. They describe it as an entertaining read for anyone with a big data background. The author uses easy-to-understand prose while describing intriguing facts. However, some readers feel the book lacks substance and is not worth the money spent on it.

AI-generated from the text of customer reviews

45 customers mention "Insight"45 positive0 negative

Customers find the book insightful and well-written. They say it provides important concepts and intriguing facts about data mining. The prose is easy to understand, and the book encourages readers to seek more information. Readers describe it as an excellent introduction to data mining and reasoning patterns. It stimulates their minds and reinforces the importance of statistics.

"...that allow machines to use defeasible, abductive, and inductive reasoning patterns in order to operate in domains unheard of just a few years ago...." Read more

"...is a basic statistical test of causal relationship it's still a very powerful tool that I need to re-introduce in my analytical life...." Read more

"...He provides multiple historical and empirical examples of how humans are very bad at making decisions...." Read more

"...about the "New Way to Be Smart" and the potentials for vastly improved decision making that can enhance our lives, but we non-crunchers need to be..." Read more

29 customers mention "Readability"29 positive0 negative

Customers find the book engaging and instructive. They find it a great read for those with a big data background. The author provides clear explanations of using data to validate points. Overall, readers describe it as an excellent commute read.

"...of pure exhilaration, and proof again that this is the best time ever to be alive." Read more

"Great book on the importance of data-driven decision making...." Read more

"...All-in-all, I thought this book was okay. Interesting, but not great." Read more

"I thought this was one of the best books I've read this past year. I found it to be well written, entertaining and insightful...." Read more

19 customers mention "Ease of reading"16 positive3 negative

Customers find the book easy to read. They appreciate the author's clear explanations of how using data to validate points works. The book is written for novices and provides good documentation with references. The non-technical language presents history and applications in a way that's accessible to non-experts.

"This short book, which can be read in a few hours, could be considered an apology or even a manifesto for mathematical and statistical modeling...." Read more

"...The writing is clear but unexceptional. But the book is incredibly thought provoking if you haven't been aware of the trends...." Read more

"...I found it to be well written, entertaining and insightful...." Read more

"...I like the way he presented standard deviation in a way that was easy to understand...." Read more

5 customers mention "Value for money"0 positive5 negative

Customers find the book unimpressive, boring, and not informative. They say there is no substance, no story, no conclusion, and no main point.

"...All-in-all, I thought this book was okay. Interesting, but not great." Read more

"...There is no substance, no story, no conclusion and no "main point" in it. The book reads as glorified hype of something old and trivial...." Read more

"...that innocent readers like me were lured to purchase otherwise unimpressive work,..." Read more

"Not worth spending money on this book." Read more

Top reviews from the United States

  • Reviewed in the United States on September 8, 2007
    This short book, which can be read in a few hours, could be considered an apology or even a manifesto for mathematical and statistical modeling. Even those readers, such as this reviewer, who have been involved in "supercrunching" for many years will find some interesting anecdotes in this book that illustrate its power and limitations. But even more importantly, it discusses the reactions of many (and typically highly insecure) individuals against the practice of mathematical modeling, some of these bordering on the absurd but with most content with ridiculing its practice. There is no question that the "supercrunching" that the author describes will continue to have greater influence in the manner in which it is currently practiced, but it is also true that much more sophisticated approaches to modeling will arise, some of these using machine intelligence and highly advanced mathematics. Indeed, in the past decade the use of artificial intelligence has exploded in areas such as finance, network engineering, bioinformatics, and Internet security. The author has just scratched the surface of the vast number of tasks that are now being done by machine, sometimes without any intervention or supervision by humans.

    As the author details in the book, many businesses and public institutions have jumped on the bandwagon of supercrunching, and their successes in doing so he documents well, with references given for readers who want more of the details. But many businesses that could profit greatly from this approach have refrained from its use, because of skepticism or distrust of quantitative reasoning. To paraphrase the author, they want to stay with the horse-and-buggy, while others are getting around in locomotives. There is still a great reliance on "experts" whose track record is weak and when compared with statistical modeling falls very short. It is an open question whether these businesses will find themselves in bankruptcy court because of their rejection of statistical modeling, but they are going to have to face stiff competition from the businesses that do.

    For those involved in the supercrunching that the author describes, it is not surprising to hear that many important business decisions are being made based on the results of statistical modeling. Humans are to a large degree still "in the loop", but the author describes instances where the machines "are actually in charge." The author still wants both human and machine to be in a mutual symbiosis, with considerably more weight given to machine predictions. And along these same lines, he brings up the canonical question as to what place humans are to have as more responsibility for decision-making is given to the machines. Many may find their social and employment status shrink, becoming white elephants (or "potted plants" to use the author's terminology) in the process. The author tries to alleviate these anxieties by pointing to the need for humans to still do the groundwork that enables supercrunching to take place. Humans must still "hypothesize" he asserts, in that they must still make the decisions as to what variables are going to be used when the machines actually perform the statistical analysis.

    Certainly if one remains within the statistical modeling paradigm, as the author does throughout the book, there will still be need for humans to "hypothesize." But if one chooses to go beyond this paradigm, the landscape changes considerably. The author gives a brief glimpse into how this is done in his discussion on neural networks. But he leaves out any discussion of the research in automated scientific and mathematical discovery that has taken place in the last two decades, some of this research showing remarkable progress. If this trend continues, and there is every reason to think that it will, then the machines will be able to hypothesize, theorize, and analyze in a manner that is similar to humans but may be vastly superior. In addition to these developments, significant progress has been made in artificial intelligence that allow machines to use defeasible, abductive, and inductive reasoning patterns in order to operate in domains unheard of just a few years ago. These domains include automated legal reasoning, computational creativity, rumor detection and propagation, virus recognition in data networks, musical composition, and automated mortgage underwriting. With these and their supercrunching abilities, they will certainly instill both admiration and fear, and using them will require extreme confidence to a degree that goes far beyond what the author describes in this book. It is disquieting to some that the machines will have this degree of intelligence and autonomy, but to others it is a source of pure exhilaration, and proof again that this is the best time ever to be alive.
    8 people found this helpful
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  • Reviewed in the United States on June 20, 2015
    Great book on the importance of data-driven decision making. While I have always been someone that has let the data do the talking, I haven't found an easy way to explain why. Super Crunchers is that easy way! Below I have summarized some of the important points of the book....

    Super Crunching is crucially about the impact of statistical analysis on real-world decisions. Two core techniques for Super Crunching are the regression and randomization.

    1. Regression will make your predictions more accurate (Historical approach):

    It all starts with the use of regressions, and although this method is a basic statistical test of causal relationship it's still a very powerful tool that I need to re-introduce in my analytical life.

    Regressions make predictions and tell you how precise the prediction is. It tries to hone in on the causal impact of a variable on a dependent. It can tell us the weights to place upon various factors and simultaneously tell us how precisely it was able to estimate these weights.

    2. Randomization and large sample sizes (Present/Real-Time approach):

    Reliance on historical data increases the difficulty in discerning causation. Large randomized tests work because the distribution amongst the sample are increasingly identical. Think A/B testing on steroids that allows you to quickly test different combinations! Boils down to the averages of the "treated and untreated" groups.

    Government has embraced randomization as the best way to test what works. Statistical profiling led to smarter targeting of government support

    With finite amounts of data, we can only estimate a finite number of causal effects

    3. Neural network
    Unlike the regression approach, which estimates the weights to apply to a single equation, the neural approach uses a system of equations represented by a series of interconnected switches.

    Computers use historical data to train the equation switches to come up with optimal weights. But while the neural technique can yield powerful predictions, it does a poorer job of telling you why it is working or how much confidence it has in its prediction.

    Super Crunching requires analysis of the results of repeated decisions. If you can't measure what you're trying to maximize, you're not going to be able to rely on data-driven decisions.

    We humans just overestimate our ability to make good decisions and we're skeptical that a formula that necessarily ignores innumerable pieces of information could do a better job than we could.
    9 people found this helpful
    Report
  • Reviewed in the United States on March 17, 2008
    The author has a two Bachelors (Russian Studies and Economics) from Yale, PhD (Economics) from MIT, JD from Yale. He is a bonifide genius and he sprinkles this book with much of his intellectual horsepower.

    He provides multiple historical and empirical examples of how humans are very bad at making decisions. Human are even worse in determining the quality of their decisions (most people tends to exaggerate the correctness of their answers). Statistics, on the other hand, are mathetically sound way of predicting future results. Better yet, it provides a highly reliable way of determining the quality of the prediction. Generally speaking, the better the data, the more predictive statistics can become.

    Recently, however, there has been a great convergence of statistics and technology. More specifically, informational technology which allows number crunching of multiple terabytes of data in a relatively short period of time has become available at a practical price.

    The author believes whoever can take advantage of this relatively new phenomenon has a great competitive and informational edge. Having reliable data analysis tools that use statistical regression analysis enables an entitity to make better decisions (and also know the probability of making a bad decision).

    This has become a game changing convergence. Indeed, it is changing the world and will become even more important in the future.

    What should you do to be better prepared?

    Learn statistics and get the best data mining/analysis tools and employees your company can buy. As the author states, we are seeing only the beginning of this mostly positive trend.

Top reviews from other countries

  • Ashray Manohar
    4.0 out of 5 stars Very interesting read, it touches upon daily life examples ...
    Reviewed in India on February 17, 2017
    Very interesting read, it touches upon daily life examples where number crunching has played a role to extract insight of business which can be used to make crucial decisions. But at times it becomes redundant.
  • Stephen
    5.0 out of 5 stars Amazing stories. Give this out to all sorts of ...
    Reviewed in Canada on January 13, 2015
    Amazing stories. Give this out to all sorts of people. Love it!
  • TimG
    5.0 out of 5 stars Enjoyable and thought-provoking
    Reviewed in the United Kingdom on February 28, 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.
  • Den
    4.0 out of 5 stars The future is stats
    Reviewed in the United Kingdom on December 7, 2021
    Number crunching is the future. Want to get ahead? Study statistics. Great book on the brute force of number crunching and how it works.
  • Vijay Bahadur
    4.0 out of 5 stars 4 Star
    Reviewed in India on June 30, 2019
    4 star