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Naked
Naked Statistics: Stripping the Dread from the Data
Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called “sexy.” From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.
For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.
For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.
304 pages, Paperback
First published December 31, 2012
About the author
Charles Wheelan
20 books468 followersCharles Wheelan is a senior lecturer and policy fellow at the Rockefeller Center at Dartmouth College. He joined the Dartmouth faculty fulltime in June of 2012.
Wheelan’s most recent book, Naked Statistics: Stripping the Dread from the Data, was released by W.W. Norton in January of 2013. Three weeks later, it reached the New York Times bestseller list for hardback nonfiction. The San Francisco Chronicle called it a “brilliant, funny new book.” The New York Times described Wheelan as “the Dave Barry of the coin-flipping set.”
From 2004 to 2012, Wheelan was a senior lecturer in public policy at the Harris School of Public Policy at the University of Chicago. He taught several courses on understanding the policy process for Master’s students. For the 2004-05 academic year, he was voted Professor of the Year in a Non-Core Course by the Harris School student body.
In the fall of 2005, Wheelan created and taught the inaugural International Policy Practicum (IPP), in which 12 students studied economic reform in India for an academic term followed by a 10-day trip to Bangalore and Delhi to meet with economists, politicians, educators, civic leaders, and other experts. Subsequent IPPs have visited Brazil; Jordan and Israel; Turkey; Cambodia; and Rwanda and Madagascar.
In March of 2009, Wheelan ran unsuccessfully for Congress as the representative from the Illinois 5th District in the special election to replace Rahm Emanuel. In its editorial assessing the race, the Chicago Sun-Times wrote, “Voters will find a ballot filled with impressive and thoughtful candidates . . . especially Charlie Wheelan, a University of Chicago lecturer who combines a razor-sharp mind with a boatload of charm and an impressive expertise in economics and foreign policy. We expect great things from Wheelan in the future.”
Prior to joining the faculty at the University of Chicago, Wheelan was Director of Policy and Communications for Chicago Metropolis 2020, a business-backed civic group promoting healthy regional growth in the Chicago area.
From 1997 to 2002, Wheelan was the Midwest correspondent for The Economist. His story on America’s burgeoning ex-convict population was the August 10, 2002, cover story. He has written freelance articles for the Chicago Tribune, the New York Times, the Wall Street Journal and other publications.
Wheelan’s first book, Naked Economics: Undressing the Dismal Science, was published by W.W. Norton & Company in 2002. The book, an accessible and entertaining introduction to economics for lay readers, was released in paperback in September 2003 and is now published in 13 languages, including Arabic and Hebrew. The Chicago Tribune described Naked Economics as “clear, concise, informative and (gasp) witty.”
In 2007, Naked Economics was selected by 360 Degrees of Reading as one of the 360 books that every college bound student should read, alongside authors ranging from Sophocles to Malcolm X. Naked Economics was also selected as one The 100 Best Business Books of All Time by 800-CEO-READ.
Wheelan is also the author of 10 ½ Things No Commencement Speaker Has Ever Said and An Introduction to Public Policy, a comprehensive textbook on public policy published by W.W. Norton in November of 2010.
Wheelan holds a Ph.D. in public policy from the University of Chicago, a Master’s in Public Affairs from Princeton University, and a B.A. from Dartmouth College. He lives in Chicago with his wife and three children.
Wheelan’s most recent book, Naked Statistics: Stripping the Dread from the Data, was released by W.W. Norton in January of 2013. Three weeks later, it reached the New York Times bestseller list for hardback nonfiction. The San Francisco Chronicle called it a “brilliant, funny new book.” The New York Times described Wheelan as “the Dave Barry of the coin-flipping set.”
From 2004 to 2012, Wheelan was a senior lecturer in public policy at the Harris School of Public Policy at the University of Chicago. He taught several courses on understanding the policy process for Master’s students. For the 2004-05 academic year, he was voted Professor of the Year in a Non-Core Course by the Harris School student body.
In the fall of 2005, Wheelan created and taught the inaugural International Policy Practicum (IPP), in which 12 students studied economic reform in India for an academic term followed by a 10-day trip to Bangalore and Delhi to meet with economists, politicians, educators, civic leaders, and other experts. Subsequent IPPs have visited Brazil; Jordan and Israel; Turkey; Cambodia; and Rwanda and Madagascar.
In March of 2009, Wheelan ran unsuccessfully for Congress as the representative from the Illinois 5th District in the special election to replace Rahm Emanuel. In its editorial assessing the race, the Chicago Sun-Times wrote, “Voters will find a ballot filled with impressive and thoughtful candidates . . . especially Charlie Wheelan, a University of Chicago lecturer who combines a razor-sharp mind with a boatload of charm and an impressive expertise in economics and foreign policy. We expect great things from Wheelan in the future.”
Prior to joining the faculty at the University of Chicago, Wheelan was Director of Policy and Communications for Chicago Metropolis 2020, a business-backed civic group promoting healthy regional growth in the Chicago area.
From 1997 to 2002, Wheelan was the Midwest correspondent for The Economist. His story on America’s burgeoning ex-convict population was the August 10, 2002, cover story. He has written freelance articles for the Chicago Tribune, the New York Times, the Wall Street Journal and other publications.
Wheelan’s first book, Naked Economics: Undressing the Dismal Science, was published by W.W. Norton & Company in 2002. The book, an accessible and entertaining introduction to economics for lay readers, was released in paperback in September 2003 and is now published in 13 languages, including Arabic and Hebrew. The Chicago Tribune described Naked Economics as “clear, concise, informative and (gasp) witty.”
In 2007, Naked Economics was selected by 360 Degrees of Reading as one of the 360 books that every college bound student should read, alongside authors ranging from Sophocles to Malcolm X. Naked Economics was also selected as one The 100 Best Business Books of All Time by 800-CEO-READ.
Wheelan is also the author of 10 ½ Things No Commencement Speaker Has Ever Said and An Introduction to Public Policy, a comprehensive textbook on public policy published by W.W. Norton in November of 2010.
Wheelan holds a Ph.D. in public policy from the University of Chicago, a Master’s in Public Affairs from Princeton University, and a B.A. from Dartmouth College. He lives in Chicago with his wife and three children.
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January 24, 2024
Charles Wheelan tells the definition of Statistics and how we knowingly and unknowingly use Statistics in our daily life.
What I learned from this book
1) What is the point of learning statistics?
Statistics is the branch of mathematics collection, analysis, interpretation, and presentation of masses of numerical data
"To summarize huge quantities of data.
To make better decisions.
To answer important social questions.
To recognize patterns that can refine how we do everything from selling diapers to catching criminals.
To catch cheaters and prosecute criminals.
To evaluate the effectiveness of policies, programs, drugs, medical procedures, and other innovations.
And to spot the scoundrels who use these very same powerful tools for nefarious ends.”
2) Why is it said that the current method of evaluation of Professors and Teachers is deeply flawed?
The author points out all the flaws associated with the current evaluation process of Professors and teachers.
"Scott Carrell and James West, professors at the University of California at Davis and the Air Force Academy, exploited this elegant arrangement to answer one of the most important questions in higher education: Which professors are most effective?
The answer: The professors with less experience and fewer degrees from fancy universities. These professors have students who typically do better on the standardized exams for the introductory courses. They also get better student evaluations for their courses. Clearly, these young, motivated instructors are more committed to their teaching than the old, crusty professors with PhDs from places like Harvard. The old guys must be using the same yellowing teaching notes that they used in 1978; they probably think PowerPoint is an energy drink —except that they don't know what an energy drink is either. Obviously the data tell us that we should fire these old codgers, or at least let them retire gracefully.
But hold on. Let's not fire anybody yet. The Air Force Academy study had another relevant finding—about student performance over a longer horizon. Carrell and West found that in math and science the students who had more experienced (and more highly credentialed) instructors in the introductory courses do better in their mandatory follow-on courses than students who had less experienced professors in the introductory courses. One logical interpretation is that less experienced instructors are more likely to "teach to the test" in the introductory course. This produces impressive exam scores and happy students when it comes to filling out the instructor evaluation.
Meanwhile, the old, crusty professors (whom we nearly fired just one paragraph ago) focus less on the exam and more on the important concepts, which are what matter most in follow-on courses and in life after the Air Force Academy.
Clearly we need to evaluate teachers and professors. We just have to make sure that we do it right. The long-term policy challenge, rooted in statistics, is to develop a system that rewards a teacher's real value added in the classroom.
3) How did Target use Statistics to find out which all customers are pregnant?
This is one of the best examples of enhancing business using statistics. It is also the best example to show how the unethical use of statistics can affect people's privacy.
"But let's drill down for a moment on just one example of the kinds of things that the statisticians working in the windowless basement at corporate headquarters can figure out. Target has learned that pregnancy is a particularly important time in terms of developing shopping patterns. Pregnant women develop "retail relationships" that can last for decades. As a result, Target wants to identify pregnant women, particularly those in their second trimester, and get them into their stores more often. A writer for the New York Times Magazine followed the predictive analytics team at Target as it sought to find and attract pregnant shoppers.
The first part is easy. Target has a baby shower registry in which pregnant women register for baby gifts in advance of the birth of their children. These women are already Target shoppers, and they've effectively told the store that they are pregnant. But here is the statistical twist: Target figured out that other women who demonstrate the same shopping patterns are probably pregnant, too. For example, pregnant women often switch to unscented lotions. They begin to buy vitamin supplements. They start buying extrabig bags of cotton balls. The Target predictive analytics gurus identified twenty-five products that together made possible a "pregnancy prediction score." The whole point of this analysis was to send pregnant women pregnancy-related coupons in hopes of hooking them as long-term Target shoppers.
How good was the model? The New York Times Magazine reported a story about a man from Minneapolis who walked into a Target store and demanded to see a manager. The man was irate that his high school daughter was being bombarded with pregnancy-related coupons from Target. "She's still in high school and you're sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?" the man asked.
The store manager apologized profusely. He even called the father several days later to apologize again. Only this time, the man was less irate; it was his turn to be apologetic. "It turns out there's been some activities in my house I haven't been completely aware of," the father said. "She's due in August."
The Target statisticians had figured out that his daughter was pregnant before he did.
That is their business . . . and also not their business. It can feel more than a little intrusive. For that reason, some companies now ask how much they know about you. For example, if you are a pregnant woman in your second trimester, you may get some coupons in the mail for cribs and diapers—along with a discount on a riding lawn mower and a coupon for free bowling socks with the purchase of any pair of bowling shoes. To you, it just seems fortuitous that the pregnancy-related coupons came in the mail along with the other junk. In fact, the company knows that you don't bowl or cut your own lawn; it's merely covering its tracks so that what it knows about you doesn't seem so spook"
My favourite three lines from this book
“Data are to statistics what a good offensive is to a star quarterback.”
“The standard deviation is the descriptive statistic that allows us to assign a single number to this dispersion around the mean.”
“Statistics cannot be any smarter than the people who use them. And in some cases, they can make smart people do dumb things.”
What could have been better?
If you are familiar with most of the concepts of statistics, you might find this book a little boring as you won't find much new information in it. The author should have tried to avoid the sexist remarks he committed in some parts of this book.
Rating
4/5 If you are someone new to the world of Statistics, this book will give you adequate information to keep you entertained and informed at the same time.
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April 17, 2013
I couldn't get through this book, mainly because I know too much about statistics and I know too much about the specific examples he gives to illustrate his points. Unfortunately, while at times Wheelan does convey the underlying concepts of probability and statistics in a way that would help you understand them at a basic level, he does so in what I would regard as a patronizingly oversimplified way. If you compare this book to Nate Silver's book on prediction or, indeed, to the book he says motivated him (How to Lie with Statistics), this book simply doesn't deliver the goods. It clothes the concepts of statistics in yet another layer of misunderstanding and half truth. If, for example, he had spent a chapter on "unemployment" and really showed how, as a descriptive statistic, the number is meaningless for all kinds of measurement and theoretical reasons, I would have been impressed. Instead, he used it as an example of a good statistic. If he had cited Savage's "The Flaw of Averages" while making points about averages, dispersion, and distributions (the wrong points, I might add), I would have been impressed. If he had at least *mentioned* Bayes Rule and Bayesian statistics, I would have been impressed. I wasn't impressed.
May 31, 2018
Cool and easy. Even though I would have liked more advanced stuff made easy and cheesy.
June 14, 2013
There are many popular science books that try to teach basic statistical concepts, but more often than not they fall into the awful popular science trope of narrative over concepts that Malcolm Gladwell introduced into science writing and then Jonah Lehrer perfected into an awful, horrible art. Take Nate Silver's lauded book 'The Signal and the Noise'. Each chapter is about some specific area of prediction, and along the way some statistical concepts are introduced but rarely elaborated [I will note that Nate Silver only rarely mentions what the expert had for lunch during their interview, unlike much worse science books that presume we are interested in the culinary habits of scientists]. In that book, Silver also tries to make a case for Bayesian statistics over traditional statistics, but because the explanation of the concepts is not very rigorous, we don't get so much an argument as an opinion.
Charles Wheelan's book is a fantastic antidote to modern popular science writing and conceptual hand-waving. In a nutshell, the book is a stats 101 course without the math. Unlike, say, popular physics books where understanding can only be vaguely metaphorical at best without knowing quite a bit of advanced mathematics (giving the illusion of knowledge; yes, you've read "The Elegant Universe", but sorry, you still know bupkis about string theory), statistical concepts can be explained and even employed in a critical fashion without much math at all. Knowing that variation is much more informative than simply the mean doesn't require that you know calculus. Likewise for understanding simple experimental design (and most experimental designs are simple: state a null, apply Student's t-test, and you've got 70% of published scientific papers).
Of course, saying that something can be explained without math is not the same as actually doing it proficiently, but Wheelan has excelled here. The examples are all intuitive, and the writing is clear and easy. Perhaps more importantly, Wheelan spends an entire chapter on the Central Limit Theorem halfway through the book, and then uses that to explain statistical inference, sampling, and regression. Giving the Central Limit Theorem such pride of place is appropriate but is often neglected in basic statistics textbooks (not to mention popular statistics books).
The book is not flawless, but the quibbles are minor. First, Wheelan has a silly sense of humor that intrudes into the book too often (culminating in several pointless footnotes that only serve to extend jokes). Second, although there are a few mathematical appendices for various chapters, they are generally far too short and actually need more math than they have. As it is, they are likely to confuse more than help.
In general, Wheelan's book is a must read for anyone that hasn't taken a basic stats course (so every journalist ever) or can't remember much from when they did take it.
Charles Wheelan's book is a fantastic antidote to modern popular science writing and conceptual hand-waving. In a nutshell, the book is a stats 101 course without the math. Unlike, say, popular physics books where understanding can only be vaguely metaphorical at best without knowing quite a bit of advanced mathematics (giving the illusion of knowledge; yes, you've read "The Elegant Universe", but sorry, you still know bupkis about string theory), statistical concepts can be explained and even employed in a critical fashion without much math at all. Knowing that variation is much more informative than simply the mean doesn't require that you know calculus. Likewise for understanding simple experimental design (and most experimental designs are simple: state a null, apply Student's t-test, and you've got 70% of published scientific papers).
Of course, saying that something can be explained without math is not the same as actually doing it proficiently, but Wheelan has excelled here. The examples are all intuitive, and the writing is clear and easy. Perhaps more importantly, Wheelan spends an entire chapter on the Central Limit Theorem halfway through the book, and then uses that to explain statistical inference, sampling, and regression. Giving the Central Limit Theorem such pride of place is appropriate but is often neglected in basic statistics textbooks (not to mention popular statistics books).
The book is not flawless, but the quibbles are minor. First, Wheelan has a silly sense of humor that intrudes into the book too often (culminating in several pointless footnotes that only serve to extend jokes). Second, although there are a few mathematical appendices for various chapters, they are generally far too short and actually need more math than they have. As it is, they are likely to confuse more than help.
In general, Wheelan's book is a must read for anyone that hasn't taken a basic stats course (so every journalist ever) or can't remember much from when they did take it.
June 26, 2013
I have already talked about statistics here, and not in good terms. It was mostly related to Nicholas Nassim Taleb`s works, The Black Swan and Antifragile. But this does not mean statistics are bad. They may just be dangerous when used stupidly. It is what Charles Wheelan explains among other things in Naked Statistics. Naked Statistics belongs to the group of Popular Science. Americans often have a talent to explain science for a general audience. Wheelan has it too. So if you do not know about or hate the concepts of mean/average, standard deviation, probability, regression analysis, and even central limit theorem, you may change your mind after reading his book. Also you will be explained the Monty Hall problem or equivalent Three Prisoners problem or why it is sometimes better (even if counterintuitive) to change your mind.
Finally Wheelan illustrates why statistics are useless and even dangerous when the data used are badly built or irrelevant (even if the mathematical tools are correctly used!). Just one example in scientific research (which is another topic of concern to me) "This phenomenon can plague even legitimate research. The accepted convention is to reject a hypothesis when we observe something that would happen by chance only 1 in 20 times or less if the hypothesis were true. Of course, if we conduct 20 studies, or if we include 20 junk variables in a single regression equation, then on average, we will get 1 bogus statistically significant finding. The New York Times magazine captured this tension wonderfully in a quotation from Richard Peto, a medical statistician and epidemiologist: "Epidemiology is so beautiful and provides such an important perspective on human life and death, but an incredible amount of rubbish is published".
Even the results of clinical trials, which are usually randomized experiments and therefore the gold standard of medical research, should be viewed with some skepticism. In 2011, the Wall Street Journal ran a front-page story on what it described as one of the "dirty little secrets" of medical research: "Most results, including those that appear in top-flight peer-reviewed journals, can't be reproduced. [...] If researchers and medical journals pay attention to positive findings and ignore negative findings, then they may well publish the one study that finds a drug effective and ignore the nineteen in which it has no effect. [...] On top of that, researchers may have some conscious or unconscious bias, either because of a strongly held prior belief or because a positive finding would be better for their career. (No one ever gets rich or famous by proving what doesn't cure cancer. [...] Dr. Ionnadis [a Greek doctor and epidemiologist] estimates that roughly half of the scientific papers published will eventually turn out to be wrong." [Pages 222-223]
Finally Wheelan illustrates why statistics are useless and even dangerous when the data used are badly built or irrelevant (even if the mathematical tools are correctly used!). Just one example in scientific research (which is another topic of concern to me) "This phenomenon can plague even legitimate research. The accepted convention is to reject a hypothesis when we observe something that would happen by chance only 1 in 20 times or less if the hypothesis were true. Of course, if we conduct 20 studies, or if we include 20 junk variables in a single regression equation, then on average, we will get 1 bogus statistically significant finding. The New York Times magazine captured this tension wonderfully in a quotation from Richard Peto, a medical statistician and epidemiologist: "Epidemiology is so beautiful and provides such an important perspective on human life and death, but an incredible amount of rubbish is published".
Even the results of clinical trials, which are usually randomized experiments and therefore the gold standard of medical research, should be viewed with some skepticism. In 2011, the Wall Street Journal ran a front-page story on what it described as one of the "dirty little secrets" of medical research: "Most results, including those that appear in top-flight peer-reviewed journals, can't be reproduced. [...] If researchers and medical journals pay attention to positive findings and ignore negative findings, then they may well publish the one study that finds a drug effective and ignore the nineteen in which it has no effect. [...] On top of that, researchers may have some conscious or unconscious bias, either because of a strongly held prior belief or because a positive finding would be better for their career. (No one ever gets rich or famous by proving what doesn't cure cancer. [...] Dr. Ionnadis [a Greek doctor and epidemiologist] estimates that roughly half of the scientific papers published will eventually turn out to be wrong." [Pages 222-223]
September 21, 2012
This is not the most exciting book ever, but it's way more exciting than you would think for a book about statistics.
More importantly, people: YOU NEED TO KNOW THIS STUFF. This is how you separate the lies from the damn lies from the nonsense that TV news shows spew at you. I don't care if you read THIS one, but please just fucking read a book about statistics. THANK you.
More importantly, people: YOU NEED TO KNOW THIS STUFF. This is how you separate the lies from the damn lies from the nonsense that TV news shows spew at you. I don't care if you read THIS one, but please just fucking read a book about statistics. THANK you.
March 4, 2018
A solid five-star. If only I had had Charles Wheelan as my college statistics professor! :)
The synopsis on Goodreads was a good review,so I'll save some ink here.
These were all basic statistic concepts, from probability to regression. While breaking down the basic concepts, Wheelan sought to caltivate intuition around them. And he did a fantastic job. Better yet, he made me chuckle all the time with those funny, sometimes provocative real-life examples.
Some of the examples, like the route cause of the 2008 financial crisis and the Monty Hall problem, have been widely telegraphed; I’d assume Wheelan’s explanations will make them easier to sink in.
I took statistics classes during two phases of my education, but am currently using little of it at work. Given big data is on the rise, and large free data sets are becoming more obtainable, I’m toying the idea of taking data crunching as a pastime.
To challenge myself, I opted for the audiobook and played it at double speed. Not until double-digit chapters when I had to pause and rewind, because keeping four-digit numbers in my head became impossible on the noisy subway. That said, Wheelan did not lose a beat, or his cool, in breaking down more difficult hypothesis testing and multivariate regression for a lay person.
I applaud making audiobook available for math-related subjects; it would benefit visually impaired students who might otherwise find math’s too daunting. On that note, Jonathan Davis did a wonderful job with his smooth narration and sense of humor pretty much syncs with the author.
The synopsis on Goodreads was a good review,so I'll save some ink here.
These were all basic statistic concepts, from probability to regression. While breaking down the basic concepts, Wheelan sought to caltivate intuition around them. And he did a fantastic job. Better yet, he made me chuckle all the time with those funny, sometimes provocative real-life examples.
Some of the examples, like the route cause of the 2008 financial crisis and the Monty Hall problem, have been widely telegraphed; I’d assume Wheelan’s explanations will make them easier to sink in.
I took statistics classes during two phases of my education, but am currently using little of it at work. Given big data is on the rise, and large free data sets are becoming more obtainable, I’m toying the idea of taking data crunching as a pastime.
To challenge myself, I opted for the audiobook and played it at double speed. Not until double-digit chapters when I had to pause and rewind, because keeping four-digit numbers in my head became impossible on the noisy subway. That said, Wheelan did not lose a beat, or his cool, in breaking down more difficult hypothesis testing and multivariate regression for a lay person.
I applaud making audiobook available for math-related subjects; it would benefit visually impaired students who might otherwise find math’s too daunting. On that note, Jonathan Davis did a wonderful job with his smooth narration and sense of humor pretty much syncs with the author.
July 1, 2020
Excellent book for the layperson that gives you a solid grasp of statistics as well as how statistics can be used and abused. As Wheelan says in the book: Statistics don't lie, but the data behind them can because they can be faulty, misleading, or downright false.
Reading the book helps you become more critical so you won't naively believe a person or organization's argument when they cite statistics to support their case or when you read about scientific breakthroughs in the newspaper or other claims based on statistics.
Here's one interesting thing Wheelan says: about half the articles in medical journals are eventually pulled because their statistics are wrong.
Reading the book helps you become more critical so you won't naively believe a person or organization's argument when they cite statistics to support their case or when you read about scientific breakthroughs in the newspaper or other claims based on statistics.
Here's one interesting thing Wheelan says: about half the articles in medical journals are eventually pulled because their statistics are wrong.
May 16, 2020
Good intro book for aspiring to be statistician.
Simple explanation for very complicated concepts.
Simple explanation for very complicated concepts.
January 31, 2019
สนุกดี เหมาะสำหรับคนที่ไม่มีพื้นฐานทางสถิติและต้องการเข้าใจสถิติว่ามีวิธีคิดอย่างไร เล่มนี้นำเรื่องสถิติมาบอกเล่าแบบ Simplify เว่อร์ แถมยกตัวอย่างขำๆ ที่พอจะทำให้นึกภาพตามออก
น่าเสียดายที่บางตัวอย่างไม่ค่อยขำซักเท่าไหร่ เพราะเป็นมุขตะวันตกที่ผมเองบางทีก็ไม่ค่อยเก็ท (ฮา)
คงดีถ้ามีหนังสือเนื้อหาประมาณนี้เขียนโดยคนไทยบ้าง น่าจะทำให้เด็กรุ่นใหม่รักสถิติขึ้นเยอะ
น่าเสียดายที่บางตัวอย่างไม่ค่อยขำซักเท่าไหร่ เพราะเป็นมุขตะวันตกที่ผมเองบางทีก็ไม่ค่อยเก็ท (ฮา)
คงดีถ้ามีหนังสือเนื้อหาประมาณนี้เขียนโดยคนไทยบ้าง น่าจะทำให้เด็กรุ่นใหม่รักสถิติขึ้นเยอะ
March 13, 2017
A nota foi 3, porque não é possível colocar 3.5.
Ótimo livro pra quem está começando ou apenas se interessa por estatística. Eu estava buscando exatamente um livro como esse para entender melhor conceitos técnicos. O objetivo não era aprender estatística a fundo, o que você não faria com esse livro, mas entender que estatística, de verdade, é difícil, não pela matemática envolvida, mas porque quem faz estatística precisa ser atenta, sagaz e sensata.
O livro vai ficando cada vez mais técnico da metade pra Frente. O início explica tudo o que você pode encontrar na internet como estatística descritiva, modelos de posições, etc etc. já no final ele apela para as armadilhas das análises de regressão.
Algumas anotações:
- Entender o objetivo do que se quer saber com a estatística. Nem sempre os números puros conseguem te trazer respostas verdadeiras. Você precisa primeiro entender qual o objetivo que se quiser buscar pra entender se os números disponíveis conseguem te dar as respostas esperadas.
Por exemplo: descobrir se fumar causa câncer não é uma tarefa facil. Não é só separar dois grupos de pessoas que fumam (grupo de pesquisa) e outra que não fumam (grupo controle) e ver no que da. Tem que isolar exatamente a diferença entre os dois grupo. O grupo controle pode ter uma dieta ruim ou outros hábitos que podem estragar a pesquisa.
- Coeficiente de Gini mede a o quanto a riqueza (ou renda) é partilhada equitativamente dentro de um país numa escala de 0 a 1. A estatística pode ser calculada para a riquezas ou para a renda anual a nível individual ou familiar. Quanto mais perto de zero, melhor. Se toda a riqueza está concentrada em uma família, o coeficiente é um e mostra desigualdade. Se toda a riqueza está distribuída de forma igual a todos as famílias do país, o coeficiente é zero.
- "a estatística descritiva existe para simplificar, o que sempre implica alguma perda de nuance ou detalhe."
- "uma função chave da estatística é usar os dados que temos sobre perguntas mais amplas para as quais não temos informação completa. Em suma, podemos usar dados do mundo conhecido para fazer inferências informadas sobre o mundo desconhecido."
- estatística descritiva pode ser totalmente correta, mas pode trazer insights enganosos por causa do "erro das médias".
- o básico da estatística é encontrar o número moderado ou o valor do meio de uma distribuição que pode tentar trazer neutralidade na comparação de dois cenários. A medida mais básica do meio de uma distribuição é a "média".
- para distribuições sem valores atípicos sérios, ou seja, sem valores muito altos ou muito baixos do que os outros valores da distribuição, a mediana e a média serão semelhantes.
- valores absolutos geralmente podem ser interpretados dem qualquer contexto ou informação adicional. Por exemplo: saber que alguém precisou dar 80 tacadas para acertar 18 buracos no golfe. Com essa informação absoluta já é possível avaliar a performance do jogador, sem comparar outro jogador.
- valores relativos do tem significado em comparação com alguma outra coisa, ou num contexto mais amplo. Se o golfista está em nono lugar no ranking, podemos compará-lo com os oito golfistas que tiverem melhor resultado que ele em um mesmo campo, mesmo horário, mesma temperatura, etc.
- Esse resultado comparativo fica muito mais interessante se pegarmos o jogador que precisou das 80 tacadas e compararmos ele numa distribuição de percentil com os outros jogadores, conseguiremos saber se acertar 18 buracos com 80 tacadas é algo bom ou ruim. Dele cair num percentil de 90, quer dizer que ele está acima de 90% dos jogadores daquele grupo.
- A média é influência pelas medidas extremas, pelas dispersões. Um valor muito alto ou muito baixo pode influenciar a média facilmente
- a mediana não tem influência de valores extremos, mas ela não mostra o que está nos extremos
- do ponto de vista da acusação, a questão mediana versus media hora em torno de se os valores extremos numa distribuição distorcem o que está sendo descrito ou são, ao contrario, parte importante da mensagem
- se quiser descrever um grupo de números de uma forma que o faça parecer grande, focalize na média. Se quiser fazer que se pareça menor, focalize a mediana.
- a mediana não pesa observações de distância que os números se situam do ponto médio, apenas se estão acima ou abaixo
- a correlação mede o grau em que dois fenômenos estão relacionados entre si
- a Netflix consegue recomendar filmes para deus usuários porque ela relaciona os filmes que o usuário avaliou bem, com filmes assistidos e avaliações e filmes assistidos de outros clientes.
- probabilidade é o estudo de eventos e resultados envolvendo um elemento de incerteza
- probabilidades não nos dizem o que acontecerá com certeza, dizem o que é provável de acontecer e o que é menos provavelmente de acontecer.
-
Ótimo livro pra quem está começando ou apenas se interessa por estatística. Eu estava buscando exatamente um livro como esse para entender melhor conceitos técnicos. O objetivo não era aprender estatística a fundo, o que você não faria com esse livro, mas entender que estatística, de verdade, é difícil, não pela matemática envolvida, mas porque quem faz estatística precisa ser atenta, sagaz e sensata.
O livro vai ficando cada vez mais técnico da metade pra Frente. O início explica tudo o que você pode encontrar na internet como estatística descritiva, modelos de posições, etc etc. já no final ele apela para as armadilhas das análises de regressão.
Algumas anotações:
- Entender o objetivo do que se quer saber com a estatística. Nem sempre os números puros conseguem te trazer respostas verdadeiras. Você precisa primeiro entender qual o objetivo que se quiser buscar pra entender se os números disponíveis conseguem te dar as respostas esperadas.
Por exemplo: descobrir se fumar causa câncer não é uma tarefa facil. Não é só separar dois grupos de pessoas que fumam (grupo de pesquisa) e outra que não fumam (grupo controle) e ver no que da. Tem que isolar exatamente a diferença entre os dois grupo. O grupo controle pode ter uma dieta ruim ou outros hábitos que podem estragar a pesquisa.
- Coeficiente de Gini mede a o quanto a riqueza (ou renda) é partilhada equitativamente dentro de um país numa escala de 0 a 1. A estatística pode ser calculada para a riquezas ou para a renda anual a nível individual ou familiar. Quanto mais perto de zero, melhor. Se toda a riqueza está concentrada em uma família, o coeficiente é um e mostra desigualdade. Se toda a riqueza está distribuída de forma igual a todos as famílias do país, o coeficiente é zero.
- "a estatística descritiva existe para simplificar, o que sempre implica alguma perda de nuance ou detalhe."
- "uma função chave da estatística é usar os dados que temos sobre perguntas mais amplas para as quais não temos informação completa. Em suma, podemos usar dados do mundo conhecido para fazer inferências informadas sobre o mundo desconhecido."
- estatística descritiva pode ser totalmente correta, mas pode trazer insights enganosos por causa do "erro das médias".
- o básico da estatística é encontrar o número moderado ou o valor do meio de uma distribuição que pode tentar trazer neutralidade na comparação de dois cenários. A medida mais básica do meio de uma distribuição é a "média".
- para distribuições sem valores atípicos sérios, ou seja, sem valores muito altos ou muito baixos do que os outros valores da distribuição, a mediana e a média serão semelhantes.
- valores absolutos geralmente podem ser interpretados dem qualquer contexto ou informação adicional. Por exemplo: saber que alguém precisou dar 80 tacadas para acertar 18 buracos no golfe. Com essa informação absoluta já é possível avaliar a performance do jogador, sem comparar outro jogador.
- valores relativos do tem significado em comparação com alguma outra coisa, ou num contexto mais amplo. Se o golfista está em nono lugar no ranking, podemos compará-lo com os oito golfistas que tiverem melhor resultado que ele em um mesmo campo, mesmo horário, mesma temperatura, etc.
- Esse resultado comparativo fica muito mais interessante se pegarmos o jogador que precisou das 80 tacadas e compararmos ele numa distribuição de percentil com os outros jogadores, conseguiremos saber se acertar 18 buracos com 80 tacadas é algo bom ou ruim. Dele cair num percentil de 90, quer dizer que ele está acima de 90% dos jogadores daquele grupo.
- A média é influência pelas medidas extremas, pelas dispersões. Um valor muito alto ou muito baixo pode influenciar a média facilmente
- a mediana não tem influência de valores extremos, mas ela não mostra o que está nos extremos
- do ponto de vista da acusação, a questão mediana versus media hora em torno de se os valores extremos numa distribuição distorcem o que está sendo descrito ou são, ao contrario, parte importante da mensagem
- se quiser descrever um grupo de números de uma forma que o faça parecer grande, focalize na média. Se quiser fazer que se pareça menor, focalize a mediana.
- a mediana não pesa observações de distância que os números se situam do ponto médio, apenas se estão acima ou abaixo
- a correlação mede o grau em que dois fenômenos estão relacionados entre si
- a Netflix consegue recomendar filmes para deus usuários porque ela relaciona os filmes que o usuário avaliou bem, com filmes assistidos e avaliações e filmes assistidos de outros clientes.
- probabilidade é o estudo de eventos e resultados envolvendo um elemento de incerteza
- probabilidades não nos dizem o que acontecerá com certeza, dizem o que é provável de acontecer e o que é menos provavelmente de acontecer.
-
January 5, 2016
এই বইটি কিন্ডেলে সার্চ করে কিনতে গিয়ে ভুলে কিনে ফেলেছিলাম ন্যাকেড ইকোনোমিক্স। একই রকম নাম এবং হুবহু একই রকমের প্রচ্ছদই এই ভুলের কারণ। সে যাই হোক, ন্যাকেড স্টাটিস্টিকস বইটি সুখপাঠ্য, পড়ে ভালো লেগেছে। বহুল ব্যবহৃত স্টাটিসটিক্যাল এনালিটিক মেথড গুলো দৈনন্দিন জীবনের খুব সহজ উদাহরন দিয়ে আলোচনা করা হয়েছে। বেশ কিছু মজার সমস্যা আলোচনা করা হয়েছে যেমন, অসুস্থ মানুষের অসুখ কাটিয়ে উঠার উপর অন্য মানুষেদের দোয়ার প্রভাব আছে কিনা সেটা বের করার জন্য পরিসংখ্যান ব্যাবহার করতে গেলে কি কি ঝামেলায় পড়তে হয়। হার্ভার্ড বিশ্ববিদ্যালয় থেকে পাশ করা কোন সফল মানুষ, হার্ভার্ডে না পড়লে তার সফল হবার সম্ভাবনা কতটুকু সেটা কী পরিসংখ্যান দিয়ে বের করা যায় কিনা এরকম অনেক আগ্রহ উদ্দীপক বিষয় পরিসংখ্যানের দৃষ্টিকোণ থেকে আলোচনা করা হয়েছে বইটির বিভিন্ন অধ্যায়ে, তাই কিছু অধ্যায় পড়া শুরু করলে শেষ না করে উঠা যায় না। ভুলে কেনা ন্যাকেড ইকোনোমিক্স বইটিও যদি এই বইটির মত সুখপাঠ্য হয় তাহলে আর কোন আফসোস থাকবে না।
August 9, 2018
บอกไว้ก่อนว่าอ่านไม่จบ และมี bias จากการที่เรามีความรู้ทางสถิติน้อยนิดมากกกก ไม่น่าเล้ยยย ไม่น่าซื้อมา 5555
เป็นหนังสือบอกเล่าเรื่องราวทางสถิติที่อ่านค่อนข้างยากสำหรับคนที่ไม่ได้เรียนสถิติ
คือตอนแรกเข้าใจว่าจะเป็นหนังสือที่จะโยงให้เห็นสถิติที่แฝงอยู่ในชีวิตประจำวันแบบเข้าใจง่าย ตัวเลขไม่เยอะ
ปรากฏว่าก็โยงนะ ตลาดหุ้น เบสบอล งานวิจัยทั้งหลายแหล่ แต่แบบงงไปเลย สูตรการคิด สมการคณิตศาสตร์มาเต็มไปหมด คนเขียนก็พยายามตลกแต่เราไม่ตลก เรางง เราอ่านไม่จบด้วยเหอะ 😭😭😭😭
เป็นหนังสือบอกเล่าเรื่องราวทางสถิติที่อ่านค่อนข้างยากสำหรับคนที่ไม่ได้เรียนสถิติ
คือตอนแรกเข้าใจว่าจะเป็นหนังสือที่จะโยงให้เห็นสถิติที่แฝงอยู่ในชีวิตประจำวันแบบเข้าใจง่าย ตัวเลขไม่เยอะ
ปรากฏว่าก็โยงนะ ตลาดหุ้น เบสบอล งานวิจัยทั้งหลายแหล่ แต่แบบงงไปเลย สูตรการคิด สมการคณิตศาสตร์มาเต็มไปหมด คนเขียนก็พยายามตลกแต่เราไม่ตลก เรางง เราอ่านไม่จบด้วยเหอะ 😭😭😭😭
December 9, 2022
A fun introductory statistics book. But for anyone that has knowledge about statistics it is pretty basic and the author tries to be funny way too hard. There are also too many examples from American football, as if the author assumed only Americans would read the book
August 24, 2017
كتاب مُمتاز. يشرح أساسيات علم الإحصاء، دون التّعقيدات الرّياضية التي تُرافقها عادة.
بالنّسبة لي، أعتبر هذا الكتاب نقيض ما درسته في الجامعة عن الإحصاء (رياضيات جافة لم أكن أفهم الهدف منها) حيث يشرح لك بأسلوب مبسّط أهم الأدوات التي يستخدمها الإحصائيون في مجالات مُختلفة، كيف يستخرجون النّتائج التي يصلون إليها، نوعية الأخطاء التي تقع فيها الكثير من الدراسات الإحصائية، وكيف تجنّبها.
في الفصول الأولى للكتاب، سيتكوّن لديك انطباع بأن كل شيء عبارة عن "مشكل إحصائي" قابل للحل، تتقدّم مع فصول أخرى لتزيد هذه "القناعة لديك" ثم يُعرّج الكاتب على أهم المشاكل والأخطاء التي يقع فيها الإحصائيون (مجموعة محاذير يجب تجنّبها) فتفهت تلك القناعة قليلًا، حتى تصل إلى آخر الفصول التي تُعيدك إلى العالم الواقعي من جديد وتخرج بنتيجة بأن علم الإحصاء علِم قوي وتزداد أهميّته خاصّة مع التّطور التقني الذي نشهده، إلا أنّه عِلم يمتلك أدوات قويّة جدًا يُمكن إساءة استخدامها عن قصد أو عن غير قصد، مما يتسبب في مشاكل عديدة قد تصل إلى التّسبب في مقتل عشرات الآلاف بسبب دراسات إحصائية، التي نتجت عنها توصيات طبّيّة تبّينت عدم صحّتها بعد مدّة.
أنصح بقراءة الكتاب كل طالب جامعي (أو متخرّج أو غير ذلك) مهما كانت تخصّصه، وأخصّ بالذّكر المُبرمجين الذين يُمكنهم استخدام الإحصائيات بشكل أكثر كفاءة لتحليل البيانات التي تقع أيديهم عليها.
بالنّسبة لي، أعتبر هذا الكتاب نقيض ما درسته في الجامعة عن الإحصاء (رياضيات جافة لم أكن أفهم الهدف منها) حيث يشرح لك بأسلوب مبسّط أهم الأدوات التي يستخدمها الإحصائيون في مجالات مُختلفة، كيف يستخرجون النّتائج التي يصلون إليها، نوعية الأخطاء التي تقع فيها الكثير من الدراسات الإحصائية، وكيف تجنّبها.
في الفصول الأولى للكتاب، سيتكوّن لديك انطباع بأن كل شيء عبارة عن "مشكل إحصائي" قابل للحل، تتقدّم مع فصول أخرى لتزيد هذه "القناعة لديك" ثم يُعرّج الكاتب على أهم المشاكل والأخطاء التي يقع فيها الإحصائيون (مجموعة محاذير يجب تجنّبها) فتفهت تلك القناعة قليلًا، حتى تصل إلى آخر الفصول التي تُعيدك إلى العالم الواقعي من جديد وتخرج بنتيجة بأن علم الإحصاء علِم قوي وتزداد أهميّته خاصّة مع التّطور التقني الذي نشهده، إلا أنّه عِلم يمتلك أدوات قويّة جدًا يُمكن إساءة استخدامها عن قصد أو عن غير قصد، مما يتسبب في مشاكل عديدة قد تصل إلى التّسبب في مقتل عشرات الآلاف بسبب دراسات إحصائية، التي نتجت عنها توصيات طبّيّة تبّينت عدم صحّتها بعد مدّة.
أنصح بقراءة الكتاب كل طالب جامعي (أو متخرّج أو غير ذلك) مهما كانت تخصّصه، وأخصّ بالذّكر المُبرمجين الذين يُمكنهم استخدام الإحصائيات بشكل أكثر كفاءة لتحليل البيانات التي تقع أيديهم عليها.
June 4, 2013
Very engaging.
There are 3 categories of readers who would enjoy or benefit from this book:
1. People who are generally curious about things and want to know why someone might say that statistics is becoming 'sexy'.
2. People who are just starting a statistics 101 class, or are about to, and would like some motivation.
3. People who know a fair bit about statistics but who would like a little perspective and history.
Wheelan, as advertised, is an entertaining writer who sort of draws you in with little stories of statistical mysteries, mistakes, and deceptions. It's a fairly easy read, and you won't know much statistics by the end of the book, but I think you'll understand why it's an important field.
There are 3 categories of readers who would enjoy or benefit from this book:
1. People who are generally curious about things and want to know why someone might say that statistics is becoming 'sexy'.
2. People who are just starting a statistics 101 class, or are about to, and would like some motivation.
3. People who know a fair bit about statistics but who would like a little perspective and history.
Wheelan, as advertised, is an entertaining writer who sort of draws you in with little stories of statistical mysteries, mistakes, and deceptions. It's a fairly easy read, and you won't know much statistics by the end of the book, but I think you'll understand why it's an important field.
June 27, 2014
Statistics 101 with cheesy jokes.
March 23, 2019
"...คนที่มีแรงจูงใจชั่วร้าย สามารถใช้ข้อเท็จจริงและข้อมูลตัวเลขที่ดีเยี่ยมไร้ที่ติมาสนับสนุนข้อสรุปที่ยังเป็นข้อกังขาหรือผิดหลักการได้"
พยายามให้ความเข้าใจสถิติในฐานะเครื่องมือสารพัดประโยชน์ ทั้งแง่ดี และเลวร้าย แน่ ๆ ที่ได้จากเล่มนี้คืออย่าเชื่ออะไรง่าย ๆ แม้จะเป็นผลวิจัยจากหน่วยงานที่น่าเชื่อถือที่สุด ตัวเลขไม่โกหก แต่วิธีการเก็บข้อมูล ประมวลผล ผู้ใช้ ตลอดจนวิธีนำเสนอน่ะมีช่องให้เล่นแง่เยอะแยะเลย
ไม่ใช่หนังสือที่อ่านง่ายสำหรับเราที่เรียนวิชานี้มาครึ่ง ๆ กลาง ๆ แต่ก็ไม่น่าเบื่อ เพราะผู้เขียนใส่มุกมาไม่น้อยเลย
พยายามให้ความเข้าใจสถิติในฐานะเครื่องมือสารพัดประโยชน์ ทั้งแง่ดี และเลวร้าย แน่ ๆ ที่ได้จากเล่มนี้คืออย่าเชื่ออะไรง่าย ๆ แม้จะเป็นผลวิจัยจากหน่วยงานที่น่าเชื่อถือที่สุด ตัวเลขไม่โกหก แต่วิธีการเก็บข้อมูล ประมวลผล ผู้ใช้ ตลอดจนวิธีนำเสนอน่ะมีช่องให้เล่นแง่เยอะแยะเลย
ไม่ใช่หนังสือที่อ่านง่ายสำหรับเราที่เรียนวิชานี้มาครึ่ง ๆ กลาง ๆ แต่ก็ไม่น่าเบื่อ เพราะผู้เขียนใส่มุกมาไม่น้อยเลย
September 5, 2015
Being a mathematics and statistics teacher, of course I am inclined to enjoy a statistics book. There were times I found myself a bit bored because I was being explained basic statistical concepts of which I already possess a wider understanding.
This book is an excellent recommendation to students just starting statistics as it gives practical and engaging examples of statistics and easy to follow. For those who already have a broad understanding of statistical topics as well as commonly used examples of probability, this book can be repetitive and frustrating.
It also bothered me that at more than one time throughout the book Wheelan assumes that the reader does not wish to know more about these topics and so he has processed information so we don't have to. These are more of the things I would have liked to know more about but then perhaps I should have been reading a different book if that was what I was desiring.
Overall, a well written book about probability and statistics. Useful to anyone who is just starting a journey in statistics as it breaks down various statistical concepts that can be confusing to a beginner.
This book is an excellent recommendation to students just starting statistics as it gives practical and engaging examples of statistics and easy to follow. For those who already have a broad understanding of statistical topics as well as commonly used examples of probability, this book can be repetitive and frustrating.
It also bothered me that at more than one time throughout the book Wheelan assumes that the reader does not wish to know more about these topics and so he has processed information so we don't have to. These are more of the things I would have liked to know more about but then perhaps I should have been reading a different book if that was what I was desiring.
Overall, a well written book about probability and statistics. Useful to anyone who is just starting a journey in statistics as it breaks down various statistical concepts that can be confusing to a beginner.
July 27, 2015
An amusing, clear, and even fun introduction to basic statistics and probability, this gem explains foundational concepts and provides compelling examples to illuminate them. It covers correlation, normal distributions, the central limit theorem, significance, standard error, multiple regression, and so on in a way that math-phobes can likely handle without panic attacks. I wish I had read this before taking grad stats.
The truth is that students of statistics today can use Excel, SPSS, Stata and similar programs to calculate statistics. So the focus has changed from working formulas to gaining a deep understanding of the assumptions, meaning, and limitations of statistics. Wheelan's excellent book provides the background for this understanding in a readily comprehensible way.
The truth is that students of statistics today can use Excel, SPSS, Stata and similar programs to calculate statistics. So the focus has changed from working formulas to gaining a deep understanding of the assumptions, meaning, and limitations of statistics. Wheelan's excellent book provides the background for this understanding in a readily comprehensible way.
January 31, 2020
Exactly what I expected this book to be -- a compelling read on Statistics and its practical usage. The language is easy to read and examples given are super witty and relatable. For instance, I could totally relate the test vs control methodology that I'd used for one of my clients and few things that should have been more careful about. Charles shares all complex/technical stuff in Appendix at the end of respective chapters (so it's up to you if you want to get into the dirty stuff).
The book is filled with real-life examples. I'd say anyone - no matter what their profession is - will take something away from the book. For example, I'm not buying a lottery ticket, ever! or I'm not purchasing an extended warranty for my $99 printer. Charles definitely gives the reader a new lens to look at things. Post reading this, you’ll think twice before believing the outcomes of polls/surveys or sensational headlines such as “wearing blue socks improves your GMAT score”(lol).
I could somewhat link the narrative of this book with another book called Narrative and Numbers by Aswath Damodaran. Bottom-line of both of these books is: don't take statistics/numbers at their face value, use your judgement - because numbers by themselves can't!
I'd definitely love to read Charles’ other 2 books - Naked Economics and Naked Money.
The book is filled with real-life examples. I'd say anyone - no matter what their profession is - will take something away from the book. For example, I'm not buying a lottery ticket, ever! or I'm not purchasing an extended warranty for my $99 printer. Charles definitely gives the reader a new lens to look at things. Post reading this, you’ll think twice before believing the outcomes of polls/surveys or sensational headlines such as “wearing blue socks improves your GMAT score”(lol).
I could somewhat link the narrative of this book with another book called Narrative and Numbers by Aswath Damodaran. Bottom-line of both of these books is: don't take statistics/numbers at their face value, use your judgement - because numbers by themselves can't!
I'd definitely love to read Charles’ other 2 books - Naked Economics and Naked Money.
August 20, 2014
How good is this book? After reading "Naked Statistics" I wanted to teach an introductory statistics course!
I could see myself engaging the students with really cool stories, confuse them with fun probability examples, only to wittily explain it clearly a minute later. I would pursue the connection between probability and inference and they would all clearly understand hypothesis testing. I would give great tales of statistics being misused and the students and I would chuckle together over how famous researchers made mistakes. Then we would promise to never make such silly mistakes ourselves.
I would never show the students "Naked Statistics," always pretending that I came up with the examples by myself... The students would love me. The administration would love me. I would receive awards, promotions, and (finally) significant pay raises. That is how good this book is. Too bad I don't teach statistics...
I could see myself engaging the students with really cool stories, confuse them with fun probability examples, only to wittily explain it clearly a minute later. I would pursue the connection between probability and inference and they would all clearly understand hypothesis testing. I would give great tales of statistics being misused and the students and I would chuckle together over how famous researchers made mistakes. Then we would promise to never make such silly mistakes ourselves.
I would never show the students "Naked Statistics," always pretending that I came up with the examples by myself... The students would love me. The administration would love me. I would receive awards, promotions, and (finally) significant pay raises. That is how good this book is. Too bad I don't teach statistics...
January 31, 2019
I read this as a supplemental text for a statistics course. Its pretty good in terms of providing fun examples of statistics constructs, written in an accessible, punchy, relatable voice.
I think I was a little underwhelmed because other authors have written really well on the same subject e.g. The Signal And The Noise by Nate Sliver, The Black Swan (or really anything) by Nassim Nicholas Taleb and the Drunkard's Walk by Leonard Mlodinow.
I LOVED all of those books, and they are extremely tough acts to follow.
I can recommend Naked Statistics, but I urge the would-be reader to consider picking up the other books mentioned as well.
You’re not going to know statistics after you read these texts. That takes a different kind of systematic study and practice. But you will be able to relate to statistics better, and you just might become enchanted by the worldview statistics engenders.
Three of these 😐😐😐
I think I was a little underwhelmed because other authors have written really well on the same subject e.g. The Signal And The Noise by Nate Sliver, The Black Swan (or really anything) by Nassim Nicholas Taleb and the Drunkard's Walk by Leonard Mlodinow.
I LOVED all of those books, and they are extremely tough acts to follow.
I can recommend Naked Statistics, but I urge the would-be reader to consider picking up the other books mentioned as well.
You’re not going to know statistics after you read these texts. That takes a different kind of systematic study and practice. But you will be able to relate to statistics better, and you just might become enchanted by the worldview statistics engenders.
Three of these 😐😐😐
February 14, 2014
I was too frustrated with the author's tone to finish. He introduces the book by explaining why he doesn't like calculus. He recalls "schooling" his high school calculus teacher when his class was given the wrong version of the AP Calculus exam. The story felt unfitting and painted the author as a punk, and the tone continues. To paraphrase an example of accuracy vs. precision, "'Go through two lights, take a left at the second light and I'm the tthird house on the right' is accurate, 'I live 4.23 miles SW' is precise, and 'I live a really f---ing long way' is neither." Why the last example? I put this book down, disappointed.
January 19, 2021
This was juuuuuust too close to being a textbook for me to really love it.
January 30, 2021
هذا آخر كتاب في سلسلة "العراة" لـ "تشارلز ويلان" وليس أحدثها. يعرض "ويلان" فيه بأسلوبه السهل والممتع للأفكار الرئيسية في الإحصاء.
وكعادته يطرح الأفكار من خلال الأمثلة الحياتية ويمرّ بالم��ادلات الرياضية مرورًا سريعًا، ويجعل المعادلات في ملاحق قصيرة تتبع الفصول.
برأيي أنّ طبيعة الإحصاء الرياضية أثرت على قدرة "ويلان" هنا في طرح الأفكار
دون الإسهاب المملّ في مواطن، والإيجاز المخلّ في مواضع أخرى، والاضطراب في مستوى الشرح أحيانًا. وهذا ما لم أجده في كتابيه الآخرين الاقتصاد عاريًا والمال عاريًا من قبل، فقد كانا أمتع وأنفع.
لكنه كتاب جيّد لعرض فكرة جاءت في أوائل الفصول وخلاصتها أنّ قوة الإحصاء تكمن في قدرته على تبسيط المعلومات(المعطيات الرقمية) وإعطائها الشكل والمعنى، وتلك ذاتها نقطة ضعفه!
وكعادته يطرح الأفكار من خلال الأمثلة الحياتية ويمرّ بالم��ادلات الرياضية مرورًا سريعًا، ويجعل المعادلات في ملاحق قصيرة تتبع الفصول.
برأيي أنّ طبيعة الإحصاء الرياضية أثرت على قدرة "ويلان" هنا في طرح الأفكار
دون الإسهاب المملّ في مواطن، والإيجاز المخلّ في مواضع أخرى، والاضطراب في مستوى الشرح أحيانًا. وهذا ما لم أجده في كتابيه الآخرين الاقتصاد عاريًا والمال عاريًا من قبل، فقد كانا أمتع وأنفع.
لكنه كتاب جيّد لعرض فكرة جاءت في أوائل الفصول وخلاصتها أنّ قوة الإحصاء تكمن في قدرته على تبسيط المعلومات(المعطيات الرقمية) وإعطائها الشكل والمعنى، وتلك ذاتها نقطة ضعفه!
January 5, 2016
Another good Wheelan, similar to Naked Economics: Undressing the Dismal Science. There are a few topics that overlap a bit, but the author does a good job of keeping them separate. This has much of the personal anecdotes / history that make the topic more interesting, and the author includes more silly scenarios in this one which keep you engaged, such as the continually missing & crashing buses of marathon runners and sausage festival attendees. Unfortunately, the third quarter of the book gets a bit dry and stultifying when the author discusses Inference, Polling, and Regression Analysis, but the last quarter redeems itself with discussions of regression analysis mistakes and how statistics is being used to address social problems. Those discussions are once again engaging and meaningful, and include issues such as the increase in autism rate, how to tell good teachers from bad and change teacher's pay to reward the good ones, and how to reduce global poverty (this is one of the slightly overlapping bits with Naked Economics).
I also found the numerous discussions of how statistical inference can go wrong to be extremely helpful -- like any tool, you have to know what it's good for and when. Wheelan brings up that even in peer-reviewed medical journals, many if not most of the findings can't be repeated. There's also mention of a paper that finds about half the peer-reviewed papers (remember, these are supposed to be the vetted ones) are wrong, with the irony noted that if the author's right, then he's just as likely to be wrong.
I also found the numerous discussions of how statistical inference can go wrong to be extremely helpful -- like any tool, you have to know what it's good for and when. Wheelan brings up that even in peer-reviewed medical journals, many if not most of the findings can't be repeated. There's also mention of a paper that finds about half the peer-reviewed papers (remember, these are supposed to be the vetted ones) are wrong, with the irony noted that if the author's right, then he's just as likely to be wrong.
December 18, 2015
This was one of the best and simplest books about statistics I've ever read. I'm not a math major person, not a statistician and honestly I am not very interested in deep theoretical knowledge about statistics. This book helped me to understand the statistics in easy and simple way from a business viewpoint.
I rarely got bored reading it, which is hard to say for most books of statistics and economics. Because the book entertained me , enlightened me and used practical, meaningful, real life examples in each chapter
I rarely got bored reading it, which is hard to say for most books of statistics and economics. Because the book entertained me , enlightened me and used practical, meaningful, real life examples in each chapter
November 23, 2017
This book is a great primer for those who are not familiar with basic statistical concepts (in fact, I encourage you to read it as statistics can be highly manipulative). If you have completed at least one university-level statistics course or the equivalent then you can safely pass on this book (most advanced topics: OLS regression and t-statistics).
July 21, 2016
This book attempts to provide an introduction to the field of statistics in plain language in the form of stories.
I found that the method they took was too long winded. It would have been better to just explain the core mathematical concepts.
I found that the method they took was too long winded. It would have been better to just explain the core mathematical concepts.
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