- Taschenbuch: 320 Seiten
- Verlag: Bantam; Auflage: Reprint (26. August 2008)
- Sprache: Englisch
- ISBN-10: 0553384732
- ISBN-13: 978-0553384734
- Größe und/oder Gewicht: 13,2 x 1,7 x 20,9 cm
- Durchschnittliche Kundenbewertung: 3 Kundenrezensionen
- Amazon Bestseller-Rang: Nr. 185.916 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart (Englisch) Taschenbuch – 26. August 2008
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"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
From the Hardcover edition.
Companies used to rely on human experts and their years of experience to guide them. Now, cutting-edge organizations are mining the data and crunching numbers instead, to come up with more accurate, less biased predictions. As Freakonomics detailed, statistical analysis can reveal the secret levers of causation. But economist Ian Ayres argues that that's only part of the story: super crunching is revolutionizing the way we all make decisions. Beginning with examples of the mathematician who out-predicted wine buffs in determining the best vintages, and the sports scouts who now use statistics rather than intuition to pick winners, Super Crunchers exposes the world of data-miners, introducing the people and the techniques. It illuminates the hidden patterns all around us. No businessperson, academic, student, or consumer (statistically that's everyone) should make another move without getting to grips with thinking-by-numbers -- the new way to be smart, savvy and statistically superior. -- Dieser Text bezieht sich auf eine vergriffene oder nicht verfügbare Ausgabe dieses Titels.Alle Produktbeschreibungen
I find this opposition misleading because as far as most of the models from this book are concerned, 'garbage in ' garbage out' applies. That is to say that these models are made to test hypotheses, therefore it is not exact to oppose intuition to quantitative methods. Thus it is even more inexact to make the point that number crunching is superior to intuition.
Another weak point of the book is that as introductory as it might be only 6 pages out of 220 pages discuss Bayesian methods and they are to be found at the very end of the book.
However, this book provides an excellent discussion on evidence based medicine. Another very interesting part is the one where the authors points out the factors that facilitate number crunching.
In a nutshell, if you know what 'significantly different from zero' and 'everything else being equal' mean, you should be able to find a better use of your time.
Of course, for somebody who has already received training in statistical methods, there is nothing in this book from a scientific and educational point of view. And for those who have a phobia of maths: Don't worry, there is not a single equation to find.
But that somebody would be me. Still, I couldn't put it aside. And I just wish I had read this book earlier. Because if I had, statistics would have become a serious endeavor of mine. If there is a book out there putting in plain text why statistics are important not only to those who try to do serious academic research, it is definitely this one.
Why did I subtract a star? At some point this book becomes kind of redundant. For those willing to skip pages filled with information they already digested not a problem.
But to sum it up: Fun to read, especially as a primer for statistics classes. Nothing that helps you through those classes except for lots of motivation. And you might suddenly understand why _this_ review is showed to you, not any other.
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(1) Mathematical regression models generated from large datasets often generate better predictions than human experts, and they provide supporting information on the predictive weight and reliability of each explanatory variable.
(2) Well-crafted experiments using randomized trials and control groups provide good market research and behavioral analysis results.
(3) Technological advances - the Internet, massive data storage devices, rapid computation, broadband telecommunication - are making it possible to share more sources of information and create ever-larger databases for analysis.
(4) Today's companies engage in multiple forms of market research by creating and using large databases and large-scale randomized trials.
(5) Many phenomena conform to normal distributions in which 95% of the population will be found within two standard deviations of the mean, the5% balance generally divided evenly in the two tails.
That's it. I just saved you $25.00 U.S. and a half-dozen or more hours learning how a guy from Yale named Ian Ayres collected a bit of information about applied mathematical techniques that have been in practical use for decades, packaged them up, palmed them off as something new, and cooked up the ridiculous name Super Crunching to describe an ostensibly new technological development. Yet "Super Crunching" is nothing more than the author's marketing hype for a couple of standard mathematical methodologies, a creation of nothing from something. There's no new breakthrough here, no new paradigm.
Yes, the anecdotal information about the future prices of wine vintages, Capital One's teaser offerings, and evidence-based medical diagnosis are interesting (hence the two stars rating). The rest, however, is neither prescriptive nor sufficiently critically analytical. Should we go out shopping for a Super Cruncher tomorrow? Should we delight in the increased accuracy of data-driven modeling and prediction, or should we fear the implied manipulation of our desires and the incessant, single-minded drive toward maximum profit at the expense of creativity? Do we really want movies and books to be developed from mathematical models like Epagogix? Do we really want our every keystroke on the Internet to be fodder for market research that manipulates us in response? John Kenneth Galbraith, among others, warned of exogenous, manufactured demand decades ago.
SUPER CRUNCHERS is part business tome, part econometric paean, and part sociology book, but not fully any of the three. No matter how many time the author uses words like "cool" and "humongous" and "amazing," it's still regrettably a "No Sale" even for someone like me who enjoys reading about applied mathematics.
There is no discussion of how these models become abused when implemented as tools where the user of the tool has no knowledge of its limitations, when the model provides suboptimal solutions or what "outliers" are and how to deal with them (although you know immediately when you ARE the outlier and are trapped dealing with a company using a model designed for a population you don't belong to).
This leads us to becoming a nation of people who read off a screen and do what the computer says to do, while turning off our brain. Any wonder you can get outsourced in that scenario? But it must be right -- we Super Crunched it!
1. Vague definition of the term "supercrunching." Is it "super" because the author wants us to think all statistics are super, or (what I had hoped) is there something about the type of statistics to which he refers that are in fact different from statistics in decision making for the last 40 years? All the talk of large datasets implies that supercrunching is a matter of size, but then why does the very first example of regression involve a model that has only 2 predictors? No need for large data sets for this kind of a model, right? Unless the effect size is tiny, but then, what good is the model? Tell us how things really are new and different now.
2. The book reads like a list of (mostly internet) companies and how fabulous and smart they are for using statistics. Actuarial science has been around for many, many years and again we see little discussion of how the actuarial tradition has become more available outside of the insurance industry. The whole book seems more like a stream of consciousness than an organized conceptual framework about the emergence of statistics as a guide to commercial, medical, and policy making over time.
3. While perhaps an excellent lawyer and professor, the author makes so many misleading or inaccurate remarks about statistics that it could be difficult for someone with a statistics background to enjoy the book. For example, regression is discussed as a technique that is different from the "randomized test," when in fact the randomized test is a design, and the regression (more commonly the "general linear model," including regression, analysis of variance, linear and structural modeling) is the inferential statistical technique used to evaluate the results of the test design. Early in the book, the author talks about how amazing regression is, and then gives and example of how a bank evaluates probability of future actions on the phone based on past behaviors on the phone. This very first example after introducing regression does not involve regression as a prediction technique, but rather actuarial base rates! In fact, I found it very disappointing that actuarial science, probability, and Bayes' theorem (all at least as relevant to data-driven decision-making as the randomized trial) were given so little attention in the book.
4. Finally, the great irony--and part of the "this book is so bad I have to finish it" quality--is that the author writes in an incredibly intuitive manner. The book is full of cognitively biased representation of the topic, owing mainly to "availability" heuristics, for example, the authors' excessive attention to the work of his friends, his roommates, his enemies, his daughter, or the companies he shops from. Better scholarship (or at least more rational) would have involved an initial sampling of all the relevant examples and final selection of the ones that would best illustrate the concepts (which I never really understood--see points 1 and 2). As other reviewers have pointed out, there is also "confirmatory bias" all over the place (presenting only the facts that fit with one's idea)--why aren't the counter arguments and counter-evidence better presented? The author must know that people buying a book on statistics will actually be smart enough to weigh the different sides of an issue. Like I said, I read to the end just to see if there was a "punch line" where the author confesses about his unapologetically intuitive approach to writing--that the book was a joke on the reader.
I would recommend this only to people who know very little about statistics and are unaware how companies like amazon.com use statistics to improve business. Such readers will be impressed. For everyone else...there are so many better books out there. Paul Meehl would be super-disappointed in this work.
The difference in the two approaches is not just a matter of managerial preference according to the author: "We are in a historic moment of horse vs. locomotive competition where intuitive and experiential expertise is losing out time and time again to number crunching." Examples include hedge fund experts who create value by finding empirical correlations between unrelated factors and the consumer lending business where front line loan officer judgement has been replaced by more reliable centralized formulas.
I have long worked in the telecommunications business in which a surprising number of important decisions such as constructing channel line ups or marketing products is based to a large degree on experience or feeling. As we have moved to a more data-based model, we continue to struggle to achieve the balance Ayres describes as comfort with both numbers and ideas.
Ayres discusses some of the institutional and ideological barriers to such a transition. The shift to Direct Instruction in primary schools, for example, pits "the brute force of numbers" against the professional experience of teachers and the philosophical inclinations of education professionals. In the commercial lending business, super crunching (defined simply enough as "statistical analysis that impacts real-world decisions") has effectively shifted discretion from front line employees to centralized experts, has deflated salaries and has created the potential to export jobs overseas.
Overall, this is a useful discussion of the challenge of blending science and art in management. It brings a wide range of examples into play and achieves balance in its conclusions. Aside from the pure reading experience, I left with some definite plans to explore the use of randomized trials in my business in place of focus groups and simple historical analysis.
The bulk of Ayres' work consists of examples (names both companies and the software involved) within each of the sectors previously mentioned. Capital One has been running randomized tests since at least 1995 - tests include page layout, and type and size of offers. Google uses data analysis to fuel its web accelerator (uses your past browsing history to predict pages to be called up next), Wal-Mart's analysis of responses to various employment questions is used to rank potential employees, and Continental Airlines follows up on its own data to design follow-up programs for complaining fliers. Capital One's approach has also been used to evaluate various charity donation-matching programs, and could also be used to evaluate potential billboard and magazine ads. (Similarly, TiVo is now being used to evaluate various TV ads, using the same approach and measuring the relative frequency with which various ads are fast-forwarded through.)
"Offermatica" software not only automates randomization (format, type of offer) for a number of firms, it also analyzes the responses in real time, dramatically cutting the cost of experiments. Thus, no more waiting for hyper-controlled experiments in universities and laboratories that conclude, ALL OTHER THINGS BEING EQUAL (that never happens), eg. red is preferred to blue.
Randomized tests are also increasingly being used to evaluate various government programs, finding eg. that additional job location assistance more than paid for itself for those receiving unemployment benefits, guiding HeadStart programs to target those most likely to benefit.
"Super Crunchers'" health care examples were the most impressive. Don Berwick's "100,000 lives" campaign saved 122,342 lives in an 18 month period through persuading about 3,000 hospitals representing 75% of all beds to focus on six areas of improvement identified through data analyses. These included antiseptic placement of central line catheters in ICUs, elevating heads and washing the mouths of those on respirators, adoption of the latest heart attack treatments, and rapid response teams to patent beds.
Bottom Line: "Super Crunchers" is an exciting vision of what is already possible!
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