The book is written in a highly readable format, with a wry sense of humor in the narrative. At the same time book clearly feels quite dated when talking about 20's and 30's. I do think the reporting is not as bad anymore as it is described about the newspaper of early to mid 20th century.

At the same time, you will most likely run into such statistical jiggering in water hole topics and on channels like FOX. This books shows you how to critically all such information and take most of aggregated information and surveys with a grain *or mountain) of salt.

"How to Lie with Statistics" should be required reading before allowing anyone in today's world to call themselves an adult. And, yes, there should be strict testing for understanding this book before anyone is allowed to leave public (or private) schools and take part in real life.

This book shows some of the ways media such as newspapers, TV, internet, etc. decieve you. Besides the media (especially advertisers); politicians, lawyers, and all sorts of other folks behave like confidence men and try to get your money, your trust, your vote, and your beliefs.

IMMUNIZE YOURSELF! Read this book. Buy this book. Study this book. Memorize this book!

This book will help you avoid the crooked people.

This book is a must read for students and professionals, who want to see the practical aspects of Statistics. This book is well organized and along with amusing illustrations gives a great insight & introduction to the subject in totality.

Go ahead and buy it!

This brief book, written in 1954, is quite appropriate even for today. It shows how people make statistics to be what they want the interpretation to be. That is to say, it shows how people are swindled with numbers. There are, indeed, too many lies in numbers. Politicians, business leaders and the Press are very good at the tricks of twisting numbers. As Mr. Darrell Huff submits (p.9), "The crooks already know these tricks, honest men should learn them in self defense." This book will be a g great read, for those that want to be educated. (Nwankama W Nwankama)

This is a classic introduction to the language of statistics and how a few well placed numbers/graphs/terms can distort reality. I use this as a supplementary reading for my undergrad students and they love it. It helps to clarify why language, numbers, and representations are so dangerous.

It is really about how to catch when statistics are being mis-used. I first read the book when I was in high school and it had first been printed. It helped me. I still give this book as a present to the high school students in my family.

I recommend this for those in high school, especially those who are math adverse. The book helps create critical thinking skills and how to avoid many deceptions.

Statisticians hate the old adage "Lies, Damned Lies and Statistics", but statistical methods do have that reputation with the general public. There are many excellent accounts, some even understandable to laymen that explain the proper ways to analyze, study and report the analysis of statistical data. Huff's famous account is illustrative and well written. It gives the average guy a look at how statistics is commonly misused (either unintentionally or deliberately) in the popular media. Graphical abuses are particularly instructive. Readers should recognize that statistical methods are scientific and with proper education anyone should be able to recognize the good statisticians from the charletons. For now Huff's book is still a good starting place. As a statistician I hate the public image portrayed in the quote above. However, I do sometimes have fun with it myself. As I write this review I am in my office wearing a sweatshirt that reads "When all else fails manipulate the data."

A modern book by a consulting statistician on the same topic is "Common Errors in Statistics and How to Avoid Them" by Phil Good. If you enjoy this book take a look at Good's book also.

I purchased and read this book in 1957 at the age of 10. Forty one years later, I can say it was truly one of the most influetial books I've ever read. At the impressionable age of 10 and with a bent toward mathematics, it helped me learn how to "play" with numbers. A successful career later, I look back and wish I could thank Darrell Huff personally.

It's obviously not a text book on statistics, but it is an eye-opener for the young and hungry.

I first read this book 40 years ago and it is still an excellent source.
While the numbers are dated the concepts are still valid. The true name of this book should have been "How Not to be Lied to by Statistics"

I read this book on the day before I started teaching a new class in basic statistics. While I am a veteran of teaching this class, this is somewhere on the order of my fifteenth round, it has been some time since the last one. I found the book to be an excellent personal primer for teaching the course. As an Iowan, I am in the midst of the caucus fever, today is January 3, 2008, and polls and other statistical fluff are heavy in the air. By reading this book and refreshing my statistical cynicism, I will be better able to demonstrate to the students the good, bad and the ugly of how statistics can be (mis)used.

Old format and examples that have not been changed through countless reprints. Good general book for an intro. stats. class.

This book is an easy read...you can get through it in one sitting. However, the first few chapters were not as clear and concise as the later ones. I would have liked to see more explanation of the 'numbers' that the author described as problematic than what was offered in the book. The possibilities of lying with statistics is just as well presented and more informative in 'How to Lie with Charts'.

Great overview, with good examples, a little outdated with numbers. Makes you into a critical-thinking, statistically-literate citizen. I read it in a couple of days and I'm going to use parts of the book for my high school students. Best I've read!

An excellent eye-opener book, I should have read it long time ago.

I used this book 20+ years ago in a college statistics course and have kept it on my shelf since. The content is as relevant now as then and I just purchased ten copies to share with a number of my colleagues. If you are looking for a sometimes-cheeky, but always-interesting, view of how to make the facts fit the story, this is your book!

"There are three kinds of lies: lies, damned lies, and statistics." This statement, made famous by Mark Twain, describes the persuasive power of numbers and how truthful statistics can be utilized to support inaccurate claims. This is exactly what author Darrell Huff shows readers in his book, How to Lie with Statistics. Huff explains how easy it is to manipulate statistics and what a common practice this is. How to Lie with Statistics informs readers so that they are not mislead, because "the crooks already know these tricks; honest men must learn them in self-defense."
Interestingly, Darrell Huff was not a statistician. He was merely an American writer, living from 1913 to 2001. He studied sociology and journalism at the University of Iowa. Duff was once editor of Better Homes and Gardens and Liberty magazines. During his career, he wrote hundreds of "How to" articles and books.
The first chapter entitled "The Sample with a Built-in Bias" explains the errors, intentional and unintentional, that can happen while producing statistical research results and how these errors lead to influenced or inaccurate conclusions. Huff says that much of the information we read in newspapers and magazines is incorrect because the sample used is not a representative sample--one in which every source of bias has been eliminated. To support his theory, he uses an example in which Time magazine claimed that "the average Yaleman, Class of '24 makes $25,111 a year."
Huff informs his readers that there are three different types of averages and if one type is used incorrectly it can cause misinterpretation of the data and inaccurate conclusions. The word average is very loose term, not giving a reader any real information unless it is specified as to what kind of average it is--mean, median, or mode. The mean is the mathematical average or the sum of all the numbers in a series divided by how many numbers are in the series. The median tells that half the numbers in the series are above and half are below than that number. The mode is the most common number within the series. Huff provides the example of the company that boasts that their average annual pay is $5,700. This number is actually the mean. The median is only $3,000, meaning that only half the people at the company are making more than this. Furthermore, most people at the company are making $2,000, which is therefore the mode.
Huff shows how graphs are often modified to deceive readers. Misrepresentation arises when the visual representation of the data is contradictory to the numerical representation. This often happens when a person wants to "'win an argument, shock a reader, move him into action, [or] sell him something." Huff uses the example of how national income increased ten per cent in a year to demonstrate how to take a graph and present the conclusion the author desires. In both pictures below, the numbers and curve are the same. The only difference is that the bottom of the graph has been cut off, giving the impression that the line has climbed halfway up the graph.
Huff teaches his readers how to defend themselves from deceit by learning how to talk back to a statistic. The first thing he says to do is ask, "Who says so?" Often the name that is cited is not supporting or authenticating the information. It is also important to look for both conscious and unconscious bias. The next thing to do is look for how the person reporting got their information. Data, and therefore statistics, will be very inaccurate if it is drawn from a biased sample, such as those that are too small, not representative of the whole group, or that is self-selecting. The reader must search for what's missing. The absence of relevant information, such as the type of average, the base of an index, or the factor that is causing a change to occur, are clear indicators that the information may not be completely truthful. The reader needs to make sure the author has not switched subjects between the raw figure and the conclusion. Finally, the reader needs to ask himself, "Does this make sense?" If it contradicts common sense, such as an impressively precise figure, then it probably it not true.
One misperception that Huff tries to educate his readers on is the relationship between two things or the lack there of. The author says that if there is a correlation between any two incidents, meaning that a relationship is present between them without providing insight into the direction of the relationship, it does not mean that one causes the other. For example, event one could be caused by event two, event two could be caused by event one, or event one and two could be caused by an unknown factor, event three. Huff encourages readers to question relations between things or events.
How to Lie with Statistics is an outstanding book complete with tons of examples and amusing pictures. Although written over fifty years ago, this book is not outdated. Because the book is easy to read and understand, it's no wonder that it is the best selling statistics book in history. How to Lie with Statistics provides readers with a new perspective and point of view when looking at information.

Darrell Huff's How to Lie With Statistics explores all the different ways that statistics are and can be used to dupe rather than educate people. He goes into great detail thoroughly explaining each kind of "statistical lie", educating his readers so that no one will ever be lied to with statistics again. He then explains how to avoid each of these lies in one's everyday life, touching on issues such as sampling, well-chosen averages, the probable error, tricky graphs, and the post hoc fallacy.
Huff begins taking a deeper look into statistics based on samples. He points out many real-life examples in which the statistics are obtained from a sample that is not randomized. For instance, the average amount a graduate from Yale earns per year was taken from those Yale graduates that could be found, leaving out all the "barely surviving writers" and "unemployed alcoholics". As it turns out, rich people are easier to find in the first place, so there is no way that sample could give an accurate average of a Yale graduate's income. Huff's real life approach to this issue makes it easy to understand and apply to everyday life. Another point he makes, is that often when asked about income, people tend to exaggerate, something we can all understand. Huff clearly points out how important a sample is to gaining accurate statistics.
Huff moves on to discuss the idea of the "well-chosen average". Anyone who has ever bought a house has risked being lied to with statistics by a real estate agent. Huff explains this "trick" in detail, which can help anyone looking into real estate, or even a new job. A real estate agent may say that the average income in a certain neighborhood is $20,000; however, that statistic could mean that the majority or the neighborhood has an income of $3,000 with a couple rich neighbors living down the block. Huff also points out how this method is used to fool those seeking jobs. Being close to graduation, I appreciate this information, because soon enough I am going to find myself in a situation where I need to be aware that just an average number is not sufficient, and that I should always ask for more detailed information.
One chapter that I was particularly interested in was chapter four. In chapter four, Huff discusses the probable error and the standard error. He uses the Stanford-Binet IQ test as an example to show how probable error can affect results. In his example, the error is ±3, which can mean the difference between being above average IQ or below average IQ. This struck my interest because my mother used to always tell me that I was above average; however, the average results on the IQ test are not 100, but rather, according to Huff, between 90-110. Luckily, I don't know what I scored on the IQ test when I was younger, or my confidence might take a beating.
Huff addresses the way that graphs can be used to fool and trick people. If an amount doesn't increase much between 20 and 25, then using smaller increments between the numbers on the y-axis can give the graph a greater visual impact. I often tend to look only at the line on a graph, which I thought gave me a good idea of how great the amount on the x-axis was growing. Thankfully, Huff has pointed out the trickery in graphs. I now check the numbers on each axis first before even glancing at the line, which has most likely saved me from being duped by statistics.
Huff then does an excellent job of explaining how to avoid being fooled by the post hoc fallacy. His in-depth explanation teaches the reader to take a statistic and turn it upside down and around, examining it from all sides, before assuming it is true. One study shows that smokers make lower grades than non-smokers, concluding that smoking causing a student to make lower grades. Huff teaches the reader to consider the statistic from a different angle. Couldn't it be possible that the reason those particular students are smokers is because they are making bad grades? By flipping the statistic around, one can see that the study's results may not be necessarily true. Knowing how to avoid the post hoc fallacy comes in handy in every day life, and Huff does a great job of exposing post hoc fallacy at its best.
Overall, I thoroughly enjoyed Darrell Huff's How to Lie With Statistics. I had such ease applying his examples to my everyday life, which made his ways of beating bad statistics very helpful. In addition, I find myself exposing lies that use statistics all over the place, specifically in the junk mail! I highly recommend this book because it is not only very informative, but also an enjoyable, light read.

In this small book full of funny examples, the author warns us against the danger of misuse of statistics. He advises us not to trust blindly all those means, standard error, region of confidence, etc...Indeed, at best statistics is a fabulous tool to reveal patterns that are not obvious at first sight in data, or to get information on the significance and validity of measurements. But at worse, it can be used to hide under several layers of sophisticated calculations an intrinsic flaw, or a bias in the analysis.

The book is particularly focused on elementary use of statistics in the daily context of newspapers articles on economics and business. Thus, those that are seeking examples of misconduct of statistical analysis in more quantitative fields such as scientific research might be disappointed. To those people, I advice to consult the relevant sections in 'introduction to scientific research" by E.B. Wilson for example.
Depending on your literacy in statistics you might find the book too introductory, chapters tend to stretch a little bit too much. In that case you might want to jump directly to chapter 9 (how to statisticulate), which is always interesting because it illustrates all the concepts introduced previously with many good examples.
Overall I think the author does a very good job at demystifying numbers, and at stimulating people to dig into the core of information in a pragmatic and causcious way instead of being impressed by sophisticated formulas (But of course, there would not be anything more silly than to over interpret this book and start to systematically despise the use of statistics. Statistics is an essential tool in alll disciplines of science). I put only 3 stars because there's nothing mind blowing either in the book. I was amused reading it, but I didn't learn much.

People say this book is great, but the truth is that it is very simple. There are basic skews and sample bias examples, and that is about it. If you are new to statistics, then this book is for you, but for a more advanced reader, I would look elsewhere.

If you are looking for a very general overview of some of the more mundane misuses of statistical data to prove a particular point, then this is a useful book. Since it is 50 years old, though, you won't find any up to date illustrations of the title topic.

Statistics are used in advertising extremely often, and almost as often as they are used, they are misused. In How to Lie With Statistics, author Darrell Huff describes the tendencies and strategies of how company's fool people with statistics. This book is an easy read, filled with valid, interesting information. It holds plenty of examples to help the reader understand the concepts within, and its comically illustrated for the reader's enjoyment. There may be some difficulty in fully understanding all the examples because they were all examples from the 1920's to the 1950's. For instance, people making average yearly incomes of two to three thousand dollars. The book has been in print ever since 1954, which speaks wonders about it quality and relevance even today! The author is able to discuss the lackluster subject of statistics in an entertaining way. It will certainly cause you question the reliability of the incessant statistics thrown at you on a daily basis. Huff takes a skeptical look at how most data used in advertising and other areas, is presented to fool its audience.
The key to fooling people is not to use false statistics, but rather present statistics that are so worthless to an audience that they may as well be untrue. How can so many companies get away with this procedure so much of the time? Companies are successful in deceiving people with statistics because they know that the untrained reader does not know the correct questions to ask in order to determine if the data is valid or not. They use extra minuscule print at the bottom of a page to conceal information that may significantly change how the data is viewed. They also out-right omit crucial data that is necessary to correctly interpret the given statistics.
Taking a biased sample is a quick and easy way for statistics to loose their validity right from the beginning. The book discussed an instance when Time Magazine published that the average Yale graduate from 1924 makes $25,111. Most readers will read by that kind of statistic, believing that it is accurate and fully trusting the source that broadcasts that information. In order to test the legitimacy of a statistic, one must inquire as to how that information was obtained. In this case, the writer for Time probably had no idea before including it in his article. Well, when you pick apart the way this sample was obtained, it is surprising how fast it loses all authority. Yale mailed out a survey to a sample of the graduating class of 1924, twenty-five years after commencement. The author points out first, that the university will have a much easier time contacting the very successful and well-known graduates, whereas the unsuccessful one might have fallen off the face of the earth, so to speak. He made the argument that maybe less successful graduates no longer wish to stay in contact with the school for whatever reason, if anything because they cannot afford the trips to the class reunions. Then, of the people who received the survey only a small portion of them will respond. Many recipients will immediately throw it away in the garbage, or forget about it, or be unwilling to take the time and hassle to fill it out and mail it back to the school. If someone is willing to fill it out the survey is asking a rather personal question: How much do you make in a year? This now opens the door for dishonesty. Some people out of embarrassment will exaggerate their income to feel better about themselves and to not be revealed as inferior or below average. Others might play down their income out of fear of taxes because they don't want to contradict themselves on some other document. After a biased sample has been taken, another way that the data gets skewed comes when just one kind of average is given and does not specify which average it is. There are three different averages: the mean, median, and mode. The mean is the exact average of the data. This number can be highly skewed by just one or a few individuals with extreme incomes, either much lower or much higher than normal. The median is the middle number. Half of the incomes are below this and the other half are above. The mode is the most common income in the mix. A quick way to get deceived is to be told an average and to not know which of those numbers you've been given. Companies throw the term "average" around loosely because they know that they can dish out one of these averages, and the everyday individual will not even think to ask which number he has been given. Do not think for a second that companies hold this knowledge without using it for their benefit to confuse and to falsely represent their product.
Another common method companies mislead customers is to pick one trial for display with desired results out of a myriad of others with inconclusive results. As we have been shown in class, people do not know what randomness looks like. So, when a trial comes out with eight or nine out of ten with the desired outcome, people assume that the product is a success, not realizing that in one hundred or in one thousand trials, that kind of outcome is not completely unlikely. It also helps that the number of trials is not broadcasted or advertised up front and center, if at all.
For one final note: BEWARE of pictures, graphs and diagrams. They are usually in place to mislead. Easy to find warning signs include: graphs that don't have clear units on both axes, graphs that don't start at an unclear point on either axis, graphs that don't show zero, or graphs that are so zoomed in that you cannot see any kind of long-term pattern. Also do not be fooled by pictures of doctors with medications. These are often actors wearing white lab coats.
Companies are often using statistics to fool and deceive. You should not give them the benefit of the doubt, if you are thinking about giving them any of your money. Darrell Huff shows us how people are most often fooled and how you can be on the lookout for statistical scams. This book was an enjoyable and easy enough to read that I did not want to stop reading it. It has been a trusted source of information on its subject for over fifty years. I trust it and I enjoyed it and I think you will too if you are at all interested in statistics trickery.

How to lie with Statistics covers all the uses of statistics through a hit list of it's abuses. By covering how to lie with statistics the author teaches both awareness of misleading statistics and also the basics of this branch of mathematics. It is amazing how subtle a misleading statistic can be. For example there are many types of averages, the mean median and mode. In some cases they produce different results so a statistics compiler can choose the one favorable to him or her. I hoped to get a birds eye view of statistics from this book so as to improve my awareness of the constant abuse of statistics that occurs in the media. I believe I got that and more out of this book.

Great book!.....just as I remembered when I was in school.

In the words of the author, "(...)the serious purpose that I like to think lurks just beneath the surface of this 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 (...)"

Huff presents amusing examples on how statisticians, reporters, scholars, and others distort data in order to convince people and prove false arguments. He explains how and why "When all the mistakes are in the cashier's favor, you can't help wondering." The book is funny and gives a nice understanding of the way statistics can and should be used, for bad and for good.

An excellent read! However, if you are looking for an introduction to statistical terms and concepts, this is not the book. You should take a look at 'Cartoon guide to statistics' by Larry Gonick, they form a good combination!

This book is a classic. I recommend it to anyone who is going to deal with the topic of statistics. It is brief and entertaining. You do not need any prior knowledge of sttistics or strong math background to enjoy this book.

This is a magnificently clear and engaging book, which readers with no knowledge of statistics will find invaluable for working out when they're being taken for a ride.

If you do happen to have some knowledge of stats, though, there's nothing new here, although you might find the illustrative examples interesting.

A classic first edition description of how to skeptically review projections, and the reason why this view is so true. This book was first published in 1954 and all of its comments remain very poignant.

not quite what i was expecting, but then i probably would have discovered that if i had read other reviews! i'm planning on taking a statistics course this fall and wanted a quick refresher before the class. this was recommended to me a fun general stat book, so i purchased it. it really is a quick and fun read. mr. huff's examples are a little dated, but still very relevant. if anything it makes it more fun to read with the outdated statistics and goofy illustrations. the chapters are clearly presented and it's an excellent very general introduction to statistics. for something more broad i would recommend "practical statistics simply explained", also available at amazon.

Who said numbers have to be dull? This book was funny, very funny, when it was first published fifty years ago.
It has aged well. It's still funny - and what with Enron and others having "lied with statitics", there's a lot that one can learn from this extraordinary little book.

This book was recommended to me, tangentially, on the subject of 'software engineering project management metrics'. Part of the point of the recommendation was to understand how seemingly irrefutable metrics can be used to persuade, mislead, and worse - support forgone conclusions.

This books teaches that well... "Numbers, when tortured, will confess to anything". It teaches how numbers can misrepresent, how to spot when that is occuring, and how to garner the real information the numbers might be telling you.

I read this book in 2 evenings - maybe 90 minutes each evening. For about the attention span of a few TV shows, I have gained some knowledge that will give me a new perspective in many important situations.

If I were to pick this book up in a bookstore, a quick glance would have made it seem... outdated. the graphics, the language, and some of the facts anf figures are from the 1930's-1950's... but if you get past that, it is more relevant than ever, because we all encounter abuse of statistics every day. (I heard a radio ad the other day that said people in a certain career make 'up to $60,000 or more a year on average'... What does that MEAN? Up to OR MORE? You could stick ANY number between those two statements and have it be true. On AVERAGE? Isn't an average a hard and fast calculated value? Doesn't EVERY career have an 'average salary' of 'up to $60,000 or more'? Heck... doesnt' every career have an 'average salary' of 'up to $1,000,000 or more'? Just another example of tortured numbers...)

If you were paying attention in school, you don't need to read this.

Fortunately, the author knows that a lot of people were not paying attention to the long and boring classes that tried to teach us complex math we would never use. This book is short and full of things everyone should know. You don't need to be good at math or anything else to learn from it.

The book is 50 years old, but math (and lies) don't change. Not only is the book full of knowledge, but you learn that swindlers at all levels have been using the same lies for at least 50 years, but in reality much longer.

This is a good book for people that have no math/statistics background at all. If you read it, you are certain to notice some advertisement, "news report," or political activist using one of the lies within a day or two. The only reason someone might not like this book is if they already know about everything in it. But if you don't know why people correctly say both "statistics don't lie" and "...lies, damn lies, and statistics," this book is for you.

-- with no equations. This book really is for every one. In fact, if you're a no-equations reader, this book will be especially helpful.

It shows all the little tricks that advertisers and propagandists, government agencies included, throw at you every day. One, p.85, is an impressive sounding news article about teachers' pay. At first, it looks as if a generous government outlay had doubled or tripled teachers' salaries. Looking closer, however, one sees an odd cluster of unrelated numbers flying in close formation. None of the numbers quoted has any bearing on any other, at least none that the article's reader can discover.

Duff also points out the fallacy of correlation. Oh, it's a useful enough measure, if (!) a number of mathematical requirements are met. It is not causation, however. For example, there is a strong correlation between a school child's height and the child's score on a given spelling test - taller kids do better. The fact is a lot less surprising when you see that first graders tend to be smaller than sixth graders, and tend to know fewer words. Maybe the example sounds silly, but no sillier than lots of the numbers in the news every day.

This is a quick and approachable read, and true even if the examples are now dated. Despite its name, this book really is aimed at honest people, readers who want real understanding of the data thrown at them, and presenters who want their numbers to be understood properly. And best, you don't have to be a mathematician to see what's going on.

//wiredweird

A more appropriate title would be, "Self defense against people lying with charts and statistics" This book is a great light primer on ways others can manipulate statistics to use against you.

This is important, as Mark Twain asserted, "There are 3 types of lies: Lies, Damn Lies, and Statistics." There are so many techniques available to manipulate statistics, one might first disregard them all. But with all due respect to Mr. Twain, statistics really are important, so having this book to understand some of these techniques is vital.

Just take the lesson with a grain of salt. The book is thin, making it an easy read, but the lessons of universe are not contained in it's pages. Best to view it as the first class of Statistical Self Defense 101, rather than a master course.

I read this book when it first came out, fifty years ago, and thought it was hilarious. The examples are a little dated by now, but it is still plenty of fun. You don't need a math background to understand it. Maybe it's most enjoyable for grade school kids, but grown-ups can look at it too.

this book was wonderful!

we had to read it for math but it was actually interesting and made sense in everyday life!

i'll read it again and again.

This book gives a briljant understanding in the relation between the numbers of statistics (abstract things for many students and even professionals) and what they actually mean. Far from adopting the idea that numbers hold the 'hard truth' the author shows how (sometimes obvious, sometimes subtle) different ways of representing the data can lead to completely different conclusions, or at least perceptions by the reader. Everyone that aims to publish statistic results either in science, advertising or the media should read this book to learn to realise the power of representation.
But not only the content is a reason to read it: the book is short, it reads easily and is often funny

Many say we live in a world that is advanced. Some would have us believe that everything can be measured and understood. However if this is the case, why are we presented with such diverse explanations and remedies for the "simplest" things from everyday life?

This book does not attempt to give you the answers to any of the questions you might have about the world. The book does an outstanding job of giving insight into understanding the arguments people use to attempt to manipulate us into adopting their beliefs.

Most of the techniques of manipulation are simple and old. They are often as simple as drawing a graph that while technically accurate, is completely misleading. Another simple technique is collecting a statistic that in reality has little to say about something else, yet due to a historical coinsidence they appear to be related. This book is a catalog of techniques used to distort the truth and to convince people to believe in what is not true.

The book is small and easily read in a couple of hours. It is as suitable for a 12 year old as it is for a college professor. Use it not to be taken in by those who seek to manipulate you. Read it long before you vote, read it before you panic over over the latest doom and gloom reported on the news or in the paper, read it before you are taken in by the latest and greatest hype that is designed to snag you and your money.

When you are done, please pass it along to your kids or a friend!

How to Lie with Statistics, by Darrel Huff, should be required reading for everyone. The cachet of numbers are used all the time in modern society. Usually to end arguments--after all, who can argue with "facts"? Huff shows how the same set of numbers can be tweaked to show three different outcomes, depending on where you start and what you use. The fundamental lesson I learned from this book is that mathematical calculation involves a whole set of conditions, and any number derived from such a calculation is meaningless without understanding those conditions.

He also mentions that colleagues have told him that the flurry of meaningless statistics is due to incompetence--he dispatches this argument with a simple query: "Why, then, do the numbers almost always favor the person quoting them?" Huff also provides five questions (not unlike the five d's of dodgeball) for readers to ask, when confronted with a statistic:

1. Who says so?

2. How does he know?

3. What's missing?

4. Did somebody change the subject?

5. Does it make sense?

All this is wrapped up in a book with simple examples (no math beyond arithmetic, really) and quaint 1950s prose. In addition humor runs from the beginning (the dedication is "To my wife with good reason") to the end (on page 135, Huff says "Almost anybody can claim to be first in something if he is not too particular what it is"). This book is well worth a couple hours of your time.

Although "How to Lie with Statistics" is a bit dated (having been written in the 1950's), the principles it puts forth are still valid today--if not moreso than ever--and the material is delivered in clear, concise, and even entertaining anecdotes and illustrations.

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 you are a visual person -who prefers graphics and charts to text- and have taken no statistics course in your entire life, this book is a perfect fit for you. If you are a well-educated statistician, but do not know how to apply your tricks in advertisement or publishing industry, the book will work for you, too. Finally, if you are graphic designer working for one of the magazines or creating charts for corporate reports, you can also have a lot of fun by just realizing that now a lot of people know about your tricks. Although the book is written more than 50 years ago, it is still very up-to-date, due to the concept it is targeting: people are still trying to make you believe in things that do not exist by using fancy charts and unrealistically accurate numbers.

Despite the fact that this book is well over 30 years old, it is still right on target. The examples given are a bit dated, but still do a good job in illustrating the principle.

You do not need a background in any kind of statistics to understand and appreciate the lessons in this book.

It's nice to read a book like this. There is a very light tone through the book. And it's a short book, incidentally. You will finish it in about 2 sessions.
I was happy to see that I'm not the only person annoyed at the way graphs are shown, with the bottom cut off to dramatize changes. This is only one example out of several. You woul be able to figure out some of these yourself, but not all of them. One was a bit deep and I had to read it a second time to understand what the falsity was.

I recommend this book because it imparts valuable information in a readable fashion. Statistics are everywhere, and you absolutely must know how to separate the chaff.

This book is one of those little volumes that everyone should read at least once.

I go through the darned thing every once in a while, sometimes on rainy afternoons when I have nothing else to do. I always feel just a little bit smarter for the effort.

The author champions a critical point-of-view that I've used to great advantage in countless meetings, arguments, and discussions.

It reads sort of like an ancient "New Yorker" magazine; familiar, breezy, a little subversive. The dated examples always make me smile, like I'm reading a book I swiped from my graduate advisor 30 years ago.

You won't be disappointed. Honest. It's a short read, but I wish all my time was as well spent!

How to Lie with Statistics by Darrell Huff gives an explanation of common statistical errors. The book is clearly written and is understandable to a reader without a mathematics or statistics background. At only one hundred and forty two pages the book is a quick and easy read.

The book was originally published in 1954. The many copious examples were current at the time of writing, but are extremely dated now. Depending on the readers attitude this may be distracting, or faintly amusing. The advanced age of the examples does not make the text any harder to understand.

While the examples are dated, the concepts appear to be timeless. The same statistical manipulations still seem to be going on nearly fifty years later. The Author covers a wide range of statistical errors, or abuse. All of the types of errors will be familiar to anyone who pays attention to the news, or has seen an advertisement that uses numbers.

How to Lie with Statistics gives the reader the knowledge to detect common statistical skulduggery. If this knowledge were more widely spread, perhaps advertisers, political spinmiesters and sloppy journalists would not be able to get away with that sort of abuse.

Since our schools regularly let us through without a single course in statistics, this book is for the general reader who is in peril of learning facts that aren't facts. It won't teach you statistics, but it will teach you what to look out for when you read the paper and see numbers and graphs. Since most institutions who report these data care little whether they are accurate or significant, you must rely on yourself to determine whether they are good numbers.

The problems with statistical data are still relevant today, and it is shocking to realize how contemporary many of his examples seem. The problems of bias, averaging, and confusing correlation with causation all dupe even the most well-educated people, and the advantage lies with the person who can spot fallacies and not be fooled. While learning statistics would be ideal, this book shows the first step towards understanding and critiquing statistical data. It is not longer or more complicated than it should be, and is simple to understand. Still, if you don't know how to evaluate some of the simple data that you come by every day in the news, this book will provide you with infinite wisdom.

I recently reread this book and am still enthralled. Huff wishes to arm the reader with enough knowledge to spot inappropriate statistics. Even though this book is almost half a decade old, it is as timely today as ever. And it is amusing to see references to costs from the middle of the last century. Anyone who wishes to understand the world around them, and I hope that's all of us, should read this book. It is a classic.

Huff has found a catchy title to lure readers into the dry-sounding but utterly fascinating world of statistics.

Packed with examples from the 1950s which seem as fresh as yesterday, this book shows how advertisers and others try to trick us by misusing statistics. "Figures don't lie, but liars figure." could be the unwritten motto of this book. Almost every day I see a Gee Whiz graph which makes something look like it's skyrocketing out of control before I check the scale. Then it turns out that a 5% or 10% bump looks like 600% to 1,000%. The trick is to abuse the visual impact of the graph.

A company might cut wages 30%, then later increase them 30% and claim that they have restored the cut. Huff explains why this is actually a 9% pay cut! This is one of many weapons in the statisticulator's arsenal.

Huff, aided by Geis's amusing and revealing illustrations, teaches us how to defend ourselves from statistical abuse, by showing us how the tricks are done.

This short essay is really interesting, even if it relies much more above use that humans make of statistics than above statistics itself. You can deceive people by presenting statistical data by mainly two methods: not representative samples and inaccurate calculations, and both are fairly, but not deeply, examined within the text. You can easily realize that the book was written in 1954 (half a century ago!), but it is still a suitable to almost absolute beginners in statistics.

This book, written in 1954, is just as pertinent today (perhaps even more so, as it's so easy to acquire statistics due to our current technology) -- Darrell Huff gives people the tools to talk back to statistics. Though there is a little bit about deliberate deception, in such things as "The Gee-Whiz Graph" (about how the graphical display of statistics can be twisted so that one can get any desired result, though the stats aren't changed), the meat of the book is regarding sound statistical reasoning, something that people today really need to consider.

For example, every person who listens to the latest survey showing a correlation between certain food and certain health problems or benefits should read "Post Hoc Rides Again", in which people erroneously leap from statistical correlation to a cause-and-effect relationship. An example given in the book is a report in which it was found that smokers had lower grades in college; ergo, said the researcher, smokers wishing to improve their grades should quit smoking! Of course, a statistical study showing that there's a "significant" relation between smoking and low grades doesn't show which causes the other -- perhaps educational failure draws people to smoke! My own theory would be that the =type= of person who is given to smoking is also given to not doing well in school; instead of cause and effect, one has a correlation from a shared, third (and unnamed) cause. One comes across these fallacies in the news =every=day=; I've been reading my online news, and in the science section I've already found two suspicious cause-and-effect reports. As Huff notes, it's not the statistics which are in question -- it's how they're used.

Some of the figures and examples used are funny due to their datedness (I love the picture of the surveyor asking a doctor what brand of cigarette he smokes, and the cigar-smoking baby just makes me smirk). It seems to me if you multiply every monetary amount by 10, you might get a better idea as to what it's worth (I don't know what it is actually worth, as I don't know what the inflation from 1954 is (another suspicious statistic)).

More to the point, with the help of this book, you need not have blind faith in the numbers or disgustedly throw all stats away. The mathematics of statistics guarantees them to have great power, as long as you know how to interpret them correctly. You might be pleasantly surprised to find that more common sense than math is involved in this book, but the truth is most modern abuse of numbers happens well after the numbers have been calculated. Of course, once you talk back to statistics people may think you're crazy; at least you won't be fleeced by false reasoning.

Along with that saying "Lies, Damn Lies and Statistics" this book lets the public know that there are methods out there that distort and can mislead. As a statistician who knows that the proper use of statistical methods is valuable and uncovers truth or quantifies uncertainty I get a bit worried about the continued association of statistics with lies. However, this book by Huff is entertaining and is a classic. If you read it carefully you will see that it is not statistical methods that create the lie but rather unscrupulous people who misuse the methods and take advantage of the public's ignorance of statistical ideas. The message of the book is to learn statistics so that you won't be deceived!