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Numbersense: How to Use Big Data to Your Advantage [Kindle Edition]

Kaiser Fung
4.5 out of 5 stars  See all reviews (2 customer reviews)

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Book Description

How to make simple sense of complex statistics--from the author of Numbers Rule Your World

We live in a world of Big Data--and it's getting bigger every day. Virtually every choice we make hinges on how someone generates data . . . and how someone else interprets it--whether we realize it or not.

Where do you send your child for the best education? Big Data. Which airline should you choose to ensure a timely arrival? Big Data. Who will you vote for in the next election? Big Data.

The problem is, the more data we have, the more difficult it is to interpret it. From world leaders to average citizens, everyone is prone to making critical decisions based on poor data interpretations.

In Numbersense, expert statistician Kaiser Fung explains when you should accept the conclusions of the Big Data "experts"--and when you should say, "Wait . . . what?" He delves deeply into a wide range of topics, offering the answers to important questions, such as:

  • How does the college ranking system really work?
  • Can an obesity measure solve America's biggest healthcare crisis?
  • Should you trust current unemployment data issued by the government?
  • How do you improve your fantasy sports team?
  • Should you worry about businesses that track your data?

Don't take for granted statements made in the media, by our leaders, or even by your best friend. We're on information overload today, and there's a lot of bad information out there.

Numbersense gives you the insight into how Big Data interpretation works--and how it too often doesn't work. You won't come away with the skills of a professional statistician. But you will have a keen understanding of the data traps even the best statisticians can fall into, and you'll trust the mental alarm that goes off in your head when something just doesn't seem to add up.

Praise for Numbersense

"Numbersense correctly puts the emphasis not on the size of big data, but on the analysis of it. Lots of fun stories, plenty of lessons learned—in short, a great way to acquire your own sense of numbers!"
Thomas H. Davenport, coauthor of Competing on Analytics and President’s Distinguished Professor of IT and Management, Babson College

"Kaiser’s accessible business book will blow your mind like no other. You’ll be smarter, and you won’t even realize it. Buy. It. Now."
Avinash Kaushik, Digital Marketing Evangelist, Google, and author, Web Analytics 2.0

"Each story in Numbersense goes deep into what you have to think about before you trust the numbers. Kaiser Fung ably demonstrates that it takes skill and resourcefulness to make the numbers confess their meaning."
John Sall, Executive Vice President, SAS Institute

"Kaiser Fung breaks the bad news—a ton more data is no panacea—but then has got your back, revealing the pitfalls of analysis with stimulating stories from the front lines of business, politics, health care, government, and education. The remedy isn’t an advanced degree, nor is it common sense. You need Numbersense."
Eric Siegel, founder, Predictive Analytics World, and author, Predictive Analytics

"I laughed my way through this superb-useful-fun book and learned and relearned a lot. Highly recommended!"
Tom Peters, author of In Search of Excellence



Product Description

About the Author

KAISER FUNG is a professional statistician withover a decade of experience applying statistical methods to marketing and advertising businesses. He is an adjunct professor at New York University teaching practical statistics. He is the creator of the popular Junk Charts blog and the author of the acclaimed Numbers Rule Your World.


Product details

  • Format: Kindle Edition
  • File Size: 5854 KB
  • Print Length: 224 pages
  • Simultaneous Device Usage: Up to 4 simultaneous devices, per publisher limits
  • Publisher: McGraw-Hill; 1 edition (11 July 2013)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B00COKLSX2
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: #295,188 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most Helpful Customer Reviews
By Robert Morris TOP 100 REVIEWER
Format:Hardcover
I agree with an observation by Mark Twain: "Figures often beguile me, particularly when I have the arranging of them myself; in which case the remark attributed to Disraeli would often apply with justice and force: `There are three kinds of lies: lies, damned lies and statistics.'" There has been an abundance of books and articles published in recent years that discuss the emergence and significance of Big Data. According to Kaiser Fung, "Big Data is real, and its impact will be massive. At the very least, we are all consumers of data analyses. We must learn to be smarter consumers. What we need is Numbersense." In his opinion, those who generate the best analyses of data possess both technical ability and business acumen...and "Numbersense is the third dimension."

What exactly is it? "Numbersense is that noise in your head when you see bad data or bad analysis. It's the desire and the persistence to get close to the truth. It's the wisdom of knowing when to make a U-turn, when to press on, but mostly when to stop. It's the awareness of where you came from, and where you're going. It's gathering clues, and recognizing decoys. The talented ones can find their way from A to Z with fewer wrong turns. Others struggle and get lost in the maze, possibly never finding Z...The best way to nurture Numbersense is by direct practice or by learning from others. I wrote this book to help you get started. Each chapter is inspired by a recent news item in which someone made a claim and backed it up with data."

When I first read this explanation, I really did not fully "get it" but after reading the book, I appreciated as well as understood what Numbersense is...and isn't. In a phrase, I view it as "street smarts for data consumers.
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4.0 out of 5 stars Number Sensible 16 Sept. 2013
Format:Kindle Edition
Review courtesy of [...]

"Big Data has essentially nothing to say about causation. It's a common misconception that an influx of data flushes cause-effect from its hiding place."

You recently graduated from a law school, and are still searching for a job. You get a voicemail from your school telling you that they are conducting a survey of whether recent graduates have gotten jobs. If you do not respond, they will assume you have a job. Do you bother to call them back to tell them the disappointing news?

Odds are, you don't. That's why law schools use this and other techniques to game the law school metrics, disingenuously boosting their entrance GPAs and LSAT scores, reputational reviews, and post-graduation employment statistics. Too often, unfortunately, those metrics are taken at face value.

In Numbersense, Kaiser Fung argues that we are in the age of Big Data - an age of extensive, personalized information useful for purposes including marketing, economics, and sports, but also a source of confusion, doubt, and increased evidence for theories both good and bad. Numbersense is the willingness to probe behind headline figures and decide if the data is actually meaningful, whether law school statistics or the unemployment rate. We turn to data for answers, but it is too often overwhelming, misleading, or evidence only of correlation, not causation.

The last point is perhaps the most critical. Target, a large shopping chain, was so effective at predicting pregnancy from consumption patterns they accidentally informed parents before the daughter had herself let them know - a triumph for Big Data, if something of an awkward one. Unfortunately, this doesn't mean buying a large purse causes pregnancy, but simply that they correlate.
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Amazon.com: 4.6 out of 5 stars  18 reviews
8 of 9 people found the following review helpful
3.0 out of 5 stars interesting read on some fascinating ways people in marketing, sports, and academia have manipulated data to their advantage 5 Aug. 2013
By Jenni M. Parks - Published on Amazon.com
Format:Hardcover
I was really drawn to this non-fiction selection by Fung because I work in the IT industry and my specialty is analytics. Numbersense promises to be the book that reconciles Big Data and business decisions, guiding readers into harnessing data to answer important questions. While I found the book to be well written and technically accurate, it left me with a bit of confusion. Fung spends a good portion of the book illustrating how data scientists in the pocket of marketers can manipulate the story told by the underlying raw data through careful selection and application of specific statistical procedures and quantifications that don't lie per se but simply give impressions that other procedures and quantifications might contradict. Lesson learned: Don't just trust the data scientists; always, always have a look at the raw data and review the statistical methods used so that you can get the whole picture instead of just what the data scientists presenting the data want you to see. Ok, good. But Fung goes on to detail in later chapters examples in which raw data, on the whole, turns out to be entirely misleading because there is too much "noise" in the quantifications to get a realistic understanding of the relationships between the variables. In these examples, he shows readers clearly that actionable information can only be extracted from the raw data by carefully selecting and applying the best statistical procedures and quantifications for the given questions we are trying to ask of our data. Lesson learned: sometimes looking at the raw data is not helpful at all and you need to rely on skilled data scientists to select the right procedures and quantifications to make sense of the data. And now we are left with two lessons that are in potential conflict. Which of course begs the question, how are members of the intended audience - business folks without a deep statistical background- supposed to know whether the raw data is:

A. going to be useful in helping us determine whether our data scientists are clever little devils gaming us

or

B. too scary and noisy to tell us anything unfiltered and we need to trust our data scientists to intelligently apply the "right" procedures and quantifications to make sense of it all for us

If Fung, who is by all impressions, a brilliant thinker and writer, can address this question in a revision of the original text, I'd be much more comfortable reclassifying Numbersense as a handy go-to guide on making sense of Big Data instead of a light and interesting read on some fascinating ways people in marketing, sports, and academia have manipulated data to their advantage.
5 of 5 people found the following review helpful
5.0 out of 5 stars How to cope with an information blizzard that has become a data tsunami 28 July 2013
By Robert Morris - Published on Amazon.com
Format:Hardcover
I agree with an observation by Mark Twain: "Figures often beguile me, particularly when I have the arranging of them myself; in which case the remark attributed to Disraeli would often apply with justice and force: `There are three kinds of lies: lies, damned lies and statistics.'" There has been an abundance of books and articles published in recent years that discuss the emergence and significance of Big Data. According to Kaiser Fung, "Big Data is real, and its impact will be massive. At the very least, we are all consumers of data analyses. We must learn to be smarter consumers. What we need is Numbersense." In his opinion, those who generate the best analyses of data possess both technical ability and business acumen...and "Numbersense is the third dimension."

What exactly is it? "Numbersense is that noise in your head when you see bad data or bad analysis. It's the desire and the persistence to get close to the truth. It's the wisdom of knowing when to make a U-turn, when to press on, but mostly when to stop. It's the awareness of where you came from, and where you're going. It's gathering clues, and recognizing decoys. The talented ones can find their way from A to Z with fewer wrong turns. Others struggle and get lost in the maze, possibly never finding Z...The best way to nurture Numbersense is by direct practice or by learning from others. I wrote this book to help you get started. Each chapter is inspired by a recent news item in which someone made a claim and backed it up with data."

When I first read this explanation, I really did not fully "get it" but after reading the book, I appreciated as well as understood what Numbersense is...and isn't. In a phrase, I view it as "street smarts for data consumers."

These are among the dozens of passages that caught my attention, shared to provide a sense of the thrust and flavor of Kaiser Fung's lively and eloquent narrative:

o On Basic Requirements: "Numbersense is that bit of skepticism, urge to probe, and desire to verify. It's having the truffle hog's nose to hunt the delicacies. Developing Numbersense takes training and patience. It is essential to know a few basic statistical concepts. Understanding the nature of means, medians, and percentile ranks is important. Breaking down ratios into components facilitates clear thinking. Ratios can also be interpreted as weighted averages, with those weights arranged by rules of inclusion and exclusion. Missing data must be carefully vetted, especially when they are substituted with statistical estimates. Blatant fraud, while difficult to detect, is often exposed by inconsistency." (Page 53)

o On the Problem with the Problem: "Shrouded in the fog of war, we are losing sight of the problem we are trying to solve. [For example,] Obesity is not the adversary; rather, it is early death caused by obesity-related diabetes such as diabetes and stroke. This distinction is crucial. We can win the battle against obesity and still lose the battle on mortality." (65)

o On Restaurants That Will Benefit from Coupon Promotions: "New entrants, with few loyal customers, have little to lose, and offer the best value for the money. Some established restaurants use the promotion to fill empty seats during the low season. These are the places that serve special menus offering anything but their regular fare. These are the places that can make money from those one-time meals. They aren't counting on the return business." (94)

o On Priming Effects (i.e. exposure to a stimulus influences a response to a later stimulus): "So many things could predispose one's behavior. Multiple priming effects may be in effect simultaneously. The effect may only last for some unknown amount of time. Even after the effect has been demonstrated, people would not believe that they have been affected. The results from various experiments threaten the search for stable, logical, causal structures that explain our decisions. [Check out Daniel Kahneman's Thinking, Fast and Slow.] The absence of explanations consigns statisticians to modeling correlations, an activity that is inherently prone to errors not curable by data infusion." (125)

o On an Uninformed General Public: "Journalists on the economics beat have yet to wake up to Big Data. The Bureau of Labor Statistics makes public indices covering geographic regions, expenditure groups, and various definitions of inflation rates, and yet we seldom hear about them in the news. Disaggregation unwinds the averaging process, and the component indices tend to make more sense to us. When data id plentiful, we should appreciate the diversity of its components. Two strategies that sometimes backfire are averaging and filtering. The former stamps out the variety while the latter casts dark shadows." 171)

o Final Thoughts: "I can't leave you with the idea that everyone must become data analysts to survive the era of Big Data. That is not the logical conclusion of this book. I do warn you that the wide availability of data will bring confusion and invite mischief. I hope you won't take data at face value ever again, and you see the power of looking under the hood." (201)

I agree with Fung that the need to develop Numbersense is more urgent now than ever before as what was once characterized as an information blizzard has since become a data tsunami. Here in a single volume is probably about all the information, insights, and counsel anyone needs to do that. As Kaiser Fung correctly suggests, in the new world of Big Data, "there is no escape from people hustling numbers." True, but knowing how to make better decisions using better information is not only possible...it is imperative. Years ago, after parents of Harvard students vehemently protested against a tuition increase, then president Derek Bok responded, "If you think education is expensive, try ignorance."
3 of 3 people found the following review helpful
4.0 out of 5 stars A book as fun as Freakonomics... 15 July 2013
By Booklover Mom - Published on Amazon.com
Format:Hardcover
"The key is not how much data is analyzed, but how."

Data is manipulatable. The same set of data can be analyzed to give the exact polar results. With the accessibility of the Internet, we are living in a world of lots of data. "Big Data" is the word the author used. It's a vast number of data that's beyond the scope of any normal data analysis program can handle or manage. Lots of data are obtainable, with lots of analyses of these data available, since every single one of the market players are studying these data to gain an edge in competition.

The author used the Gates Foundation's example to let us know that even big organizations with lots of money and analysts can still make a stupid decision with the wrong data or analysis. Ten years ago, the Foundation made a mistake assuming that smaller school s are better for student achievement, which is later proven untrue. He argued that Big Data moves us backwards, since more data results in more time spend analyzing, arguing, validating and replicating results. More of the any above activities will cause more doubt and confusion. Therefore, It's urgent to learn a way to analyze them so you can just keep your head clear, and not being lied to.

"Any kind of subjective ranking does not need to be correct, it just has to be believed"

What do we believe, and what technique do we use to help us make the decision? Data analysis is an art, and not every statistician knows what he's talking about. A person with good "numbersense" will be way above the others in avoiding the pitfall. A person with a good numbersense will spot bad data or bad analyses, or know when to stop when collecting his own. Unfortunately, numbersense can't be taught in a regular classroom, a program or a textbook. It's only learned from another person or real life practices. After more than 20 years in management in a hospital, I know these people do exist, but rarely. They are wonderful problem solvers. Lucky for rest of us, this book is a great place to start learning about numbersense. The author has a way of explaining complex subjects in a simple and understandable way, and his flow of thoughts is logical and very easy to follow. While analyzing data, the author also explained statistical terms thoroughly, as the term significant does not necessarily means important.

The author used real life news examples where someone made a claim about something and then backed it up with data, and he analyzes them, explaining the process to us along the way. The examples include: Law schools admission data, Groupon's business model, diets and BMI, unemployment and jobs, our inability to remember prices and CPI, and even fantasy football. These examples were very interesting to read as the author gives step-by-step instructions of how these data we see everyday could easily be manipulated to fool us. My daughter is in the process of applying for college, and I can assure you, after reading the first chapter, I will never look at college rankings the same way.

I think every person in marketing, business, sociology, management or data analysis should read this book, as well as any consumer who wants to make sense of this so called "Big Data." Numbersense is a great word for people who have the talent of analyzing data and spotting errors or intended manipulation. This book reads very much like Freakonomics: A Rogue Economist Explores the Hidden Side of Everything by Stephen Levitt, but is a bit more technical and might take a little understanding of statistics and/or business to fully appreciate the book. My background is business administration and healthcare, and I had a fun ride.

*Thanks to Netgalley in providing the advanced reading copy.
1 of 1 people found the following review helpful
5.0 out of 5 stars Fun and informative 14 Nov. 2013
By Michel Baudin - Published on Amazon.com
Format:Hardcover
This book is less about how to use big data to your advantage than about spotting errors and attempts to deceive. It makes it both fun to read and informative. The author whets your appetite with a prologue about airlines misinterpreting data on flight delays, and then goes on to expose the games deans play to to boost their universities' rankings and make the employment of recent grads look better than it is. He then goes on to debunk claims about obesity and diets in the US, and the measurements of the effects of various marketing tactics.

When he moves on to economic data, on the other hand, he is much less severe with the adjustments and filters applied to statistics on jobs and prices, and explains how the data would be much more misleading without them. I skipped Part IV, about Sporting data. It is largely about fantasy football, which I know nothing about, but the rest of the book was well worth the time I spent on it.
1 of 1 people found the following review helpful
5.0 out of 5 stars Show me how to question the numbers I hear on TV. Great book. 2 Oct. 2013
By C. Chiu - Published on Amazon.com
Format:Hardcover
I bought this book because I liked Fung's first book. This is a really nice follow up. I often question the numbers I hear on TV but can't really say why they are wrong. Fung gives me the words I can use to make my case. It's eye-opening to learn of the many ways the universities game the rankings. I learn why Groupon is a stupid idea. Also, unemployment rate seems wrong because the government rigs the counting rules. You only have to work one hour to be counted! There is a lot of great stories in this book. Don't miss this.
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