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Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart Audio CD – Abridged, Audiobook
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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.
From the Hardcover edition.
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In this groundbreaking new book, “Super Crunchers”, the author, Ian Ayre, believes that the days of making decisions by relying on intuitions are gone. Today’s best and brightest organizations are analyzing massive databases at lightening speed and gaining greater insights into our human behavior. They are the super crunchers. Companies like Google and Amazon have amassed huge data about our behavior. They know our tastes better than we do, they understand our children’s needs, they know all about the shoes and clothes we buy and wear. They even know how much we’ll pay for our flights and whether the fares will go up or down. They are a new breed of decision makers who are now calling the shots.
But how do these companies arrive at such massive information. Obviously, they need a huge amount of data to get comprehensive conclusions, and the data should be as randomized as possible to avoid bias. Interestingly, such data is now available through specialized companies whose business is to collect figures from computer charts, store receipts, company reports and the like, and sell them to researchers. They have data on the size of our shoes, the foods we eat, the medicines we buy, the games we play and even the colors we favor. Once the data is collected, researchers use regression and statistical analysis to evaluate the effect of different variables on a commodity or a behavioral trend, e.g. does music in factories increase productivity? Would a salary raise improve employee loyalty…..? And so on.
Putting This new data-analysis approach to many fields has made remarkable improvements. Consider the effect on the medical field. The rise of data-based decision making has reversed conventional wisdom. For example it showed that beta blockers can actually help cardiac patients and that estrogen therapy does not help aging women. Now there is a diagnostic program called “Isabel” which allows physicians to enter a patient’s symptoms into a computer and receive the most likely diagnosis. It will also tell the doctor the possible drug that caused the symptoms. Soon it will even specify the most likely therapy. No wonder many doctors are getting concerned about the possible loss of their control over diagnoses once considered a most important factor in their profession.
How are we then to look at this groundbreaking approach to decision making? One cannot dispute its speed and efficiency. It is simply remarkable! Business has put it to beneficial use, but mostly to maximize its profits from the unaware consumers. Medicine stands to gain from it technically while also helping the patient financially and health-wise. But buyer be ware! There are no guarantees and consumers have to view it critically on a case-by-case basis in order to evaluate its risks and benefits.
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.
You won't find anything ground breaking stuff here, but I can assure you that you will find the nuts and bolts of analysis, backed up with stories from the real life.
It is an easy read who also gives you a few pointers as to what other literature to read. I can highly recommend it.
"Rule of Thumb" vs Scientific Method
"Gut Hunch" vs Verifiable Methodology
If you are on the left side of each of these pairs - read this to see why "the other side" can be very helpful.
If you are on the right side of each of these pairs - read this to see why maybe "vs" should at least be "and" - using scientific method and techniques to quantify or revise the best guesses of "the experts" - combining factors that the experts consider in ways that produce repeatable and verifiable results - from baseball to wine and farther afield.
Between this book and the Numerati, I feel really smart and also super sketched out by everyone knowing what I buy.
Probably should stop leaving reviews.