Why is It So Easy to Lie With Statistics
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Lies, Damn Lies, and Statistics: The Pirates of the Powerpoint
Darrell Huff uses a simple, but effective literary device to impress his readers about how much statistics affect their daily lives and their understanding of the world.
He does this by pretending that the book is a sort of primer in ways to use statistics to deceive, like a manual for swindlers, or better, for pirates. He then pretends to justify the crookedness of the book in the manner of the retired burglar whose published rem
Lies, Damn Lies, and Statistics: The Pirates of the Powerpoint
Darrell Huff uses a simple, but effective literary device to impress his readers about how much statistics affect their daily lives and their understanding of the world.
He does this by pretending that the book is a sort of primer in ways to use statistics to deceive, like a manual for swindlers, or better, for pirates. He then pretends to justify the crookedness of the book in the manner of the retired burglar whose published reminiscences amounted to a graduate course in how to pick a lock and muffle a footfall: The crooks already know these tricks; honest men must learn them in self-defense.
This keeps the book interesting and entertaining, though for anyone even partly trained in statistics, it has very little educational value.
Of course, the title of this book and Huff's little charade would seem to imply that all such operations are the product of intent to deceive. The intelligent reader would be skeptical — it is the unfortunate truth that it not chicanery much of the time, but incompetence. On the other hand, Huff is pretty clear that the 'errors' if that is what they are always seem to come down on the side of the interested party. As long as the errors remain one-sided, he says, it is not easy to attribute them to bungling or accident.
No More Mr. Nice Guy
After being fellow pirates for much of the book, in the concluding chapter Huff finally lets go if his pet charade and faces up to the more serious purpose of the book: explaining how to look a phony statistic in the eye and face it down; and no less important, how to recognize sound and usable data in that wilderness of fraud to which the previous chapters have been largely devoted. He lays down some thumb rules, which in the end comes come down to asking intelligent questions of the stats, especially of the conclusions. Asking such questions require the readers to be aware of the tendency of stats to mislead and to not be dazzled by the numbers.
Huff's book is primarily an attempt to pull down the high estimation automatically awarded to anybody willing to quote numbers. It is a fun evening read for the expert, who may then roll his eyes and say that there is nothing of real value in the book. But as its popularity attests to, it seems to be an important book for the lay reader, just by serving a reminder that the pirates are still out there — wielding their charts.
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Yes, the book is a bit dated, but it didn't bother me in the least. So you have to increase the monetary figures to adjust for inflation, big deal. The lessons in this book are timeless.
Also, if you do want to learn how to overstate your case or misrepresent something, this book can help. It's really written to arm people with the right questions and a healthy dose of skepticism, but inevitably ends up helping the scammers as well. Making an average? Choose the one (mean, mode, median, etc.) that best represents your case. Showing a trend over time? Choose a base year that skews things the way you want. It's not hard. ...more
Written with a smile, easily understandable, yet with a fine sense of how statistics can be used against us even, allegedly, "for our own good." Every American should have a copy of HOW TO LIE WITH STATISTICS. After this book, if you'd like to get further into the nitty-gritty of numerical manipulation, how ordinary Americans are routinely deceived by it, and what you can do about it, consider INNUMERACY by John Allen Paulos.
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It seems a little shallow to rate this semi-pamphlet at four stars, as one of the must-read books, but that's exactly what I'm going to do.
This book earns four stars from me simply from its concisiveness and practicality. You can churn through this beauty in one sitting. It is entertaining, has excellent examples, introduces concepts in a wry, witty tone, and after ten years of courses, articles, books, and opinions, I have yet to learn a single thing about misleading statistics that wasn't taug
It seems a little shallow to rate this semi-pamphlet at four stars, as one of the must-read books, but that's exactly what I'm going to do.
This book earns four stars from me simply from its concisiveness and practicality. You can churn through this beauty in one sitting. It is entertaining, has excellent examples, introduces concepts in a wry, witty tone, and after ten years of courses, articles, books, and opinions, I have yet to learn a single thing about misleading statistics that wasn't taught better and quicker in this book. You will sit down with this book for an hour or two and get up from your chair having a much more educated mind about the numbers that are constantly hurled at you.
I wish that there were one of these books for every topic imaginable to man. In my mind, the perfect library has a hundred thousand of these types of book. Short, simple, clear, and distinctly important.
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The book is really short, big print, lots of illustrations, great humor and absolutely a CLASSIC.
If you had a tough time in the statistics section of your High School math class, read this to get the basics - you need it and you wil
2021-03-27 Remembering this book yet again, since it is truly "One of the Best books I have ever read." If you understand this book, and it ain't hard, and you apply it to the statistics you come across in your life, then you have a big leg up on NOT being bamboozled.The book is really short, big print, lots of illustrations, great humor and absolutely a CLASSIC.
If you had a tough time in the statistics section of your High School math class, read this to get the basics - you need it and you will benefit from it.
This was a supplementary text to my college statistics class and wow, was I glad it was. That course was not easy. Logical to the max, and useful, but it went into more depth and detail and agony of factoring and other difficult/tedious work than I can say really helped me. But this book! The simple lessons in it have stayed with me, and animate any presentation, book, technical article, advertisement, political speech, etc. etc. that I come across that uses statistics.
What could be better than that?
Oh, and did I mention that I have bought, given away and recommended this book more than almost any other book I have read?
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But in the billion years since this classic came of age, we've all learned that other ways, some of them better presented. When it was written, many people believed the information they received in the papers, in magazines, and on
Yes, it has all the stuff you hear about: how people use stats to subtly (and not-so-subtly) misdirect the reader/listener, how to systematically recognize (or create) misinterpretations, and a strong implicit call to action for clearer information in public discourse.But in the billion years since this classic came of age, we've all learned that other ways, some of them better presented. When it was written, many people believed the information they received in the papers, in magazines, and on the news. Now, news shows spend their time trying to discredit bloggers who point out their biases. Our cynicism has evolved to the point where
How to Lie With Statistics teaches valuable technique, but loses much of its insight-producing novelty.You should still read it, though, for two reasons:
- It's a classic. It's a great, simple read and you want to be able to say, "As it says in so classic and simple a book as How to Lie With Statistics--which, Professor, you have obviously studied--you are clearly hiding the truth!"
- No other book presents such a concise set of instructions for noticing when you have misled someone inadvertently. I frequently notice some document I'm preparing using a technique--quite often one built-in to popular business communication tools--that misleads people as to the real meaning of the data.
Because I've read this, I can catch myself and make sure I present my case clearly, but unimpeachably. If I mislead my audience, they'll catch me; They'll catch me and tear me apart, even if I were right.
So check out this classic, overlook its implicit innocence, learn some dirty tricks you may have forgotten or not caught, and pay attention to how you've been trained to use them just like we all have.
Bonus bit: my favorite bad statistics technique: Bar graphs with images for bars. As they grow taller, they grow wider, making a number twice as big appear four times as large.
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Definitely enjoyed this one. A clever, creative way to teach how statistics manipulate every day life. (It made numbers approachable!) The original publishing date of 1954 seriously adds to the book's charm. The numbers he uses sound ludicrous today, but also underscore his points.
Definitely enjoyed this one. ...more
For those who have fleetingly or never studied statistics, this is a good place to start. It is a quick and easily understandable read, written in plain English and with plenty of examples to prove the author's points. Personally, I have studied statist
Noting that this book was published in 1954, one may instantly discount the information as outdated. However, there are recent events that can be related to some of the examples author Darrell Huff provides, and helps to increase the book's value.For those who have fleetingly or never studied statistics, this is a good place to start. It is a quick and easily understandable read, written in plain English and with plenty of examples to prove the author's points. Personally, I have studied statistics (use and misuse) in various jobs, and have seen the positive as well as the detrimental aspects. Even with my background, I still found items of interest and was able to correlate some of Mr. Huff's thoughts to present day use.
The author's final chapter goes further than the explanations of the previous pages and outlines what the average person can do to avoid being fooled by deceptive statistics. The entire book is fun to read and informative. Four stars.
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These days, every claim is accompanied by stats to validate them. And when contradicting claims both have supporting studies behind them, you really have to stop and ask yourself what the fuck is going on. This is where this book comes to the rescue. Statisticians don't "lie" per se. But they do a lot of manipulation to b
A really fast read. And a fascinating one. Although I didn't pay attention to the release date before I began. So now I want to read another book discussing the same subject :3These days, every claim is accompanied by stats to validate them. And when contradicting claims both have supporting studies behind them, you really have to stop and ask yourself what the fuck is going on. This is where this book comes to the rescue. Statisticians don't "lie" per se. But they do a lot of manipulation to bend the truth the way they want. So it's up for the reader to know how to bust them in the act, if we ever really wanted to make informed actions.
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--This book aims to make stats applicable for the public. Thus, it targets:
a) Those who get lost with statistics for fear of mathematics and/or ignorance of its application in social issues.
…and converts them to:
b) Those who bother with statistics not for its mathematics, but for its application in social issues.
--Going easy on the mathematics is not actually sacrificing too much. Consider: public deception requires the victim thi
There's a reason why this is the best-selling popular stats book…--This book aims to make stats applicable for the public. Thus, it targets:
a) Those who get lost with statistics for fear of mathematics and/or ignorance of its application in social issues.
…and converts them to:
b) Those who bother with statistics not for its mathematics, but for its application in social issues.
--Going easy on the mathematics is not actually sacrificing too much. Consider: public deception requires the victim thinking they understand something when in fact they do not. Thus, complex math would often be counter-productive! The deception is in the lure of simple math ("1 in 3 people…!", "An increase of 200%!", "Look at this graph with a dramatic, steep curve!"), you think you understand it, while the logic behind it is manipulated...
--As for the folks who love abstract technicalities and shy away from the noisy world of human irrationality and power struggles, you'll have no problem learning the mathematics of statistics elsewhere… For the rest of us, this book is the ideal starting point. The excellent Ben Goldacre takes this to another level by applying this approach to science/public health/policy: "Pulling bad science apart is the best teaching gimmick I know for explaining how good science really works."
-Bad Science: Quacks, Hacks, and Big Pharma Flacks
-I Think You'll Find it's a Bit More Complicated Than That
-Bad Pharma: How Drug Companies Mislead Doctors and Harm Patients
The Manipulation behind the Stats:
1) Representative sample? We are sold conclusions with authoritative precision ("33.33% of users…!"), but first consider how the sample (subset of the statistical population used as an estimate) was determined:
a) Often the sample is simply too small (sampled 3 people, found 1!), so the results are biased by chance. As always, if the raw numbers of the sample size are not even provided, be suspicious. If they are given, often a cursory comparison of the sample size with the actual population size is enough to spot the lie (the expectation is the fine print goes unread).
b) More mathematical checks are mentioned only in a cursory manner and need to be supplemented elsewhere. These include the statistical significance level for handling false positives (which has limitations: effect size and reproducibility), reliability of the correlation (standard deviation/standard error…), etc.
c) Next, many methodological layers (e.g. with data collection) need to be unraveled to detect further sampling biases and non-sampling errors. And as always, the first suspicious sign is if the methodological details are hidden.
2) Which "average"? Different types have different perspectives/results; this can be unspecified or used inconsistently in comparisons: "mean" hides outliers that skew the result (think mean income where one CEO salary skews the many low-wage workers); "median" (middle value) and "mode" (most common value) are more illustrative in this case.
3) What are the visuals presenting and hiding? Just as simple math is a lure, simple visuals are full of tricks. Hidden data, ranges cut off, distorted proportions of the x/y units, distorted perspectives (ex. coloured voting map which emphasizes land mass instead of population density), etc.
4) What is the logic behind the interpretation?
a) A key methodological trick is the bait-and-switch, by making overreaching claims based on a "semi-attached figure". This is very common in health/medical claims (see Ben Goldacre), where a surrogate outcome (ex. a singular result from an isolated chemical reaction in a lab Petri dish) is falsely extrapolated as having an equal impact on a real clinical outcome (i.e. on a complex condition in a complex human body). This builds up to tremendous distortions since private companies can simply not publish negative findings (the enforcement to pre-register clinical trials is abysmal), thus creating systemic publication bias!
b) Quick glance at logical fallacies (esp. regarding correlation vs. causation). A popular one is post hoc ergo propter hoc (if B follows A, then assume A caused B).
c) The illusion of the shifting base: Ex. advertising a big % of savings that's based on a reduced new price instead of the base price. This can get really sneaky the other way around, ex. locked into paying interest routinely on the original loan amount whereas you should pay on the new decreasing amount (since you've been paying off your loan)!
d) Plenty of counter-intuitive tricks can be done with percentages. Ben Goldacre recommends using real numbers (1 out of 300) instead of percentages if you want to communicate more clearly/intuitively (esp. to describe changes). After all, an 100% increase could just be a change from 1-in-1,000,000 to 2-in-1,000,000.
The Missing:
--I have to add this note, because I've seen Bill Gates recommend this book (and novice conspiracy hacks getting the wrong ideas). Despite this book providing critical tools to spot manipulation, it presents "power" as an abstract bogeyman. Power thrives on abstraction; this is how it builds acceptance. To understand power, we must turn to political economy. Ben Goldacre provides a useful bridge, by connecting these statistical deceptions to Big Pharma, bad "science" quacks, and ugly media. For the next step:
-Talking to My Daughter About the Economy: or, How Capitalism Works - and How It Fails
-Another Now: Dispatches from an Alternative Present
Later when we learnt regression
In class 5 or 6, when we first started doing maths of finding average, mean or mode, I really had no idea what they meant or even why I was doing them. Just sum up the numbers, divide by their number and get the average or arrange them in increasing order, take the number in the middle - what they meant even. Maybe I was just following the instructions because the books said so or doing things this way will bring me marks in the exam. That's it. No more thoughts.Later when we learnt regression analysis, even then I still didn't know why I am learning this. I saw teacher writing some x & y values on the board , then their was an equation y=ax+b. There were formulas for finding a and b. I just calculated the values of a and b. Later on if it was asked in the question, I would plug in a new value of x into the equation and get a new y. That's it.
But finally after attending numerical methods, machine learning and pattern recognition courses and while doing my thesis I know why we need them and what they mean :3
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"The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify. Statistical methods and statistical terms are necessary in reporting the mass data of social and economic trends, business conditions, 'opinion polls', the census. But without writers who use the words with honesty and understanding and readers who know what they mean, the result can only be semantic nonsense… This book is a sort of primer in ways to use s
"The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify. Statistical methods and statistical terms are necessary in reporting the mass data of social and economic trends, business conditions, 'opinion polls', the census. But without writers who use the words with honesty and understanding and readers who know what they mean, the result can only be semantic nonsense… This book is a sort of primer in ways to use statistics to deceive. It may seem altogether too much like a manual for swindlers. Perhaps I can justify it in the manner of the retired burglar whose published reminiscences amounted to a graduate course in how to pick a lock and muffle a footfall: The crooks already know these tricks; honest men must learn them in self-defense." (10-11)I never cared much for statistics; I slid through Methods & Statistics 1, 2, and 3 relatively untouched, until the first time I did my own research and had to analyze the data I had so diligently collected. That was the point at which statistical analyses became meaningful to me—I had a theory and I was looking to see whether there was any evidence for it. The data itself means nothing, of course. Numbers do not mean anything on their own. What you do with the numbers, how you expose (non-)relationships between them, is when things get interesting. And tricky—as this little book sets out to show.
Huff uses humor to show a variety of ways in which statistics may be – and examples of how they have been – misleading. The book is dated by now, basing its examples mostly on stuff from the '20s through to the '50s. This did not really bother me, however, nor did I feel it detracted from the main points—if anything, it makes the book feel quaint while at the same time highlighting the fact that little has changed in how statistics are (ab)used in contemporary society. If you have a background in statistics, How to Lie is unlikely to teach you anything new about them. However, it is still worth reading if only to underscore the need to pay attention to the pervasive presence and use of statistics.
I, as someone trained in psychology, like to think that they are mostly used for good. Much knowledge has surely been gained thanks to the insights that increasingly sophisticated statistical analyses have offered. But an analysis is only as good as the way in which it is conducted, and results only as good as the way in which they are conveyed.
Huff closes his book on a more serious note, abandoning the burglar-revealing-his-trade spiel with five questions to ask when encountering statistical information (but don't worry, the chapter is still titled How to Talk Back to Statistics). They are worth listing and remembering:
1] Who says so? Look for bias: who has an interest in the statistic?
2] How does he know? Watch out for a biased/limited sample.
3] What's missing? The absence of information, like the number of cases or which kind of average was used, can render a statistic virtually meaningless.
4] Did Somebody Change the Subject? Make sure that the conclusion follows from the type of raw data that was collected.
5] Does It Make Sense? Think about what the statistic is supposed to mean/tell you, and ask whether it makes sense.
This book is both scary and highly entertaining. It's a quick but informative read based on real-life examples.
Here's one that was particularly illuminating (emphasis mine):
"Let us say that during a period in which race prejudice is growing you are employed to "prove" otherwise. It is not a difficult assignment. Set up a p
It turns out that there are so many ways one could deliberately lie with statistics, whilst simultaneously giving an air of credibility to whatever crap they are purporting.This book is both scary and highly entertaining. It's a quick but informative read based on real-life examples.
Here's one that was particularly illuminating (emphasis mine):
"Let us say that during a period in which race prejudice is growing you are employed to "prove" otherwise. It is not a difficult assignment. Set up a poll or, better yet, have the polling done for you by an organization of good reputation. Ask that usual cross section of the population if they think blacks have as good a chance as white people to get jobs. Repeat your polling at intervals so that you will have a trend to report.
Princeton's Office of Public Opinion Research tested this question once. What turned up is interesting evidence that things, especially in opinion polls, are not always what they seem. Each person who was asked the question about jobs was also asked some questions designed to discover if he was strongly prejudiced against blacks. It turned out that people most strongly prejudiced were most likely to answer Yes to the question about job opportunities. (It worked out that about two-thirds of those who were sympathetic toward blacks did not think the black had as good a chance at a job as a white person did, and about two-thirds of those showing prejudice said that blacks were getting as good breaks as whites.) It was pretty evident that from this poll you would learn very little about employment conditions for blacks, although you might learn some interesting things about a man's racial attitudes.
You can see, then, that if prejudice is mounting during your polling period you will get an increasing number of answers to the effect that blacks have as good a chance at jobs as whites. So you announce your results: Your poll shows that blacks are getting a fairer shake all the time.
The worse things get, the better your poll makes them look."
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"There is terror in numbers," writes Darrell Huff in How to Lie with Statistics. And nowhere does this terror translate to blind acceptance of authority more than in the slippery world of averages, correlations, graphs, and trends. Huff sought to break through "the daze that follows the collision of statistics with the human mind" with this slim volume, first published in 1954. The book remains relevant as a wake-up call for people unaccustomed to ex
I'm just going to quote the Amazon.com review:"There is terror in numbers," writes Darrell Huff in How to Lie with Statistics. And nowhere does this terror translate to blind acceptance of authority more than in the slippery world of averages, correlations, graphs, and trends. Huff sought to break through "the daze that follows the collision of statistics with the human mind" with this slim volume, first published in 1954. The book remains relevant as a wake-up call for people unaccustomed to examining the endless flow of numbers pouring from Wall Street, Madison Avenue, and everywhere else someone has an axe to grind, a point to prove, or a product to sell. "The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify," warns Huff.
Although many of the examples used in the book are charmingly dated, the cautions are timeless. Statistics are rife with opportunities for misuse, from "gee-whiz graphs" that add nonexistent drama to trends, to "results" detached from their method and meaning, to statistics' ultimate bugaboo--faulty cause-and-effect reasoning. Huff's tone is tolerant and amused, but no-nonsense. Like a lecturing father, he expects you to learn something useful from the book, and start applying it every day. Never be a sucker again, he cries!
Even if you can't find a source of demonstrable bias, allow yourself some degree of skepticism about the results as long as there is a possibility of bias somewhere. There always is.
Read How to Lie with Statistics. Whether you encounter statistics at work, at school, or in advertising, you'll remember its simple lessons. Don't be terrorized by numbers, Huff implores. "The fact is that, despite its mathematical base, statistics is as much an art as it is a science." --Therese Littleton
This is book aimed at the honest soul who is merely trying to make sense of the stream of numbers and stats pouring in from all around.
This is another must read.
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That being the case, I'd love to require this book for my rhetoric class. Am I over-stepping my bounds? I'm supposed to be teaching critical thinking skills, right? and I'm not sure that they'll all be taking a statistics class. I feel like when I teach graphic design or computer research or even logic, I'm doing what the experts say I ought, but can I teach statistics, too?
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This book specifically focuses on the facts and figures that we see everyday, pretty much everywhere. I thought it was well written and extremely thorough, going from problems that happen during study collection, to the cherry picking and presentation of data itself. I had to gr
I still wonder why Trigonometry and Calculus are offered in high school, but Statistics is not. It's such a broad subject that is used in so many fields-even forgetting all of the numbers we read in magazines. I digress.This book specifically focuses on the facts and figures that we see everyday, pretty much everywhere. I thought it was well written and extremely thorough, going from problems that happen during study collection, to the cherry picking and presentation of data itself. I had to grin when I noticed that some of the exact same graph and picture tricks illustrated in this book are still being used today in some of my latest issues of Time & BusinessWeek (watch out for the 3-D bar graph). Overall a great, short, and extremely easy to read book that presents a nice reminder to the darker side of stats.
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The content feels outdated. I think people lie with statistics much better today than when this was published. Yay, we're improving!
I love the title.The content feels outdated. I think people lie with statistics much better today than when this was published. Yay, we're improving!
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Duff talked about sample biases, which we can see in every day research. While his example was that of Yale graduates pay grades is a bit outdated, he shows how this sample represents a small number of people, and how it is most certainly a false representation based on logic, common sense, and science.
He then went on to dissect the differences between mean, median and mode, followed by inadequate samples (think sales pitch where you only ask 3 people versus more), tests that mislead and reveal nothing (like IQ tests), manipulation of graphs and scales, semi-attached figures, post hoc representations, and statistical manipulations. He did the same thing with each topic to show how you start with facts, manipulate it, and present it. Then he shows you how it's wool covering your eyes.
The last chapter was my favourite. It tells you how to pull the wool from your eyes and argue back against it by pointing out their chicanery. Essentially it was a recap of the previous chapters, then telling you not to be afraid to call out the BS you see in the media, advertising, or politics.
Duff does all this in a tongue-in-cheek way. He pulls no punches, nor does he sugar coat things. When I read this I could easily pick out several examples of every single thing he talked about from my own environment. Lies the media tells me to say so it's "politically correct", lies on the magazine back advert, graph manipulation in my precious National Geographic or Time Magazine... it was a disappointment to see how pervasive it is in our society. It's even more disappointing knowing that most people do not know what they are looking at and do not know that they are being swindled.
This is a book that everyone should read so they are more well informed about the world around them and can pull the wool from their eyes.
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I'm sure the information will come in useful
The only drawback is that the book was written a fair few decades ago and the language stands out from post-millennial books. I look forward to reading this book in another 10 years time. This one is also for the study shelf :)
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How often do you hear statistics bandied about in the media or used to try to prove some special-interest point? "Of course" the people quoting the figures must be r
This book was published in 1954 and some of the examples are dated but the principles it puts forth are still valid today--if not more so than ever--and the material is delivered in clear, concise, and even entertaining anecdotes and illustrations. It is also an easy read that can be easily finished in one day of concentrated effort.How often do you hear statistics bandied about in the media or used to try to prove some special-interest point? "Of course" the people quoting the figures must be right with numbers on their sides... until you look at just how those numbers were arrived at.
This book isn't truly a guide on how to lie with statistics, but it is an excellent text that informs the reader both how others will lie to them using statistics and on how to interpret the validity of purported statistical data.
If I learned one thing from business school it is how easy to mislead even with a supposedly direct topic like accounting. Actually, it is a good lesson to learn from the recent financial crisis also.
If you or someone you love believes everything they hear in the media or from scientiific journals, this is an excellent place to begin the process of obtaining wisdom.
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The book is thin, light, short and straight to the point. Overall it is easy to understand, but there are also some part of the book which I think can be written in a simpler way. Some examples need to be separated into individual paragraph, so that readers are not confused on which point the writer is actually referring to. Due to this my rating will be short of 1 star.
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It was definitely a worthwhile read. It did get me thinking about how statistics can be misused... but I do hope that some of the methods mentioned in this book are now out of date.
Some suggestions:
1 - Beware the word "average." It could mean mode, median, or true arithmetic mean. Even if it is the mean, a non-Bel
It was definitely a worthwhile read. It did get me thinking about how statistics can be misused... but I do hope that some of the methods mentioned in this book are now out of date.
Some suggestions:
1 - Beware the word "average." It could mean mode, median, or true arithmetic mean. Even if it is the mean, a non-Bell-curve distribution would probably be better represented by the median.
2 - Beware graphs. Have they been cropped to only show what the presenter wishes to show? How do you know?
3 - Beware of implied 3-dimensionality. It can cause a given multiplier to 2D representation to be interpreted as something much larger by the brain.
4 - What are you not being told? The error margins? Comparative data? A full timeline? The sample size? The sampling method?
5 - Just because a survey has found that x% of people claim that y is true... doesn't make it x% likely to be true.
...and many more, of course, but, that's a summary.
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First of all, this book was written in 1954 (!) and not by a statistician, by a journalist - the goal was to make the content as approachable to an average reader as possible. And I believe the author has succeeded - what is more, it was the first widely known book on the topic of that sort. It deserves it full 5 stars. What's more - it has aged quite well: examples are easy to grasp and properly described, even in the form of an audio-book (!).
But (there's always a "but")
No star rating. Why so?First of all, this book was written in 1954 (!) and not by a statistician, by a journalist - the goal was to make the content as approachable to an average reader as possible. And I believe the author has succeeded - what is more, it was the first widely known book on the topic of that sort. It deserves it full 5 stars. What's more - it has aged quite well: examples are easy to grasp and properly described, even in the form of an audio-book (!).
But (there's always a "but") times have changed A LOT. The "dark patterns of statistics" described by Huff (at least some of them) sound quite naive, people are better educated and do not fall to some of them. In fact, there are "new" manipulations and tricks that are harder to spot in the daylight. That's why HtLwS can't be treated as a wide-enough coverage of the topic these days.
Nevertheless, it's fun and fast to read. #goodstuff
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