- Capa comum: 144 páginas
- Editora: W. W. Norton & Company; Edição: Reissue (16 de outubro de 1993)
- Idioma: Inglês
- ISBN-10: 0393310728
- ISBN-13: 978-0393310726
- Dimensões do produto: 14 x 1 x 21,1 cm
- Peso de envio: 99,8 g
- Avaliações dos clientes: 597 classificações de cliente
- Lista de mais vendidos da Amazon: Nº 14,953 em Livros (Conheça o Top 100 na categoria Livros)
How to Lie with Statistics (Inglês) Capa comum – 17 Outubro 1993
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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
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This book couldnt be more adequade to these very interesting times we are living (2020)
Os exemplos que o livro traz são mais antigos, no entanto encaixam-se perfeitamente nas situações que deparamos no dia-a-dia.
Depois de ler este livro, e capaz de você parar de ler certos jornais como: Folha de São Paulo, OGlobo, Correio BRasiliense, Estado de Minas, Hoje em Dia, Estado de São Paulo, etc etc. Jornais extremamente tendenciosos que se portam como imparciais.
O título e o próprio autor descrevem o livro como "uma espécie de cartilha de como usar estatísticas para enganar". Isso se mostra verdadeiro caso a intenção do leitor seja essa. Porém, a leitura de suas breves 157 páginas demonstra uma intenção de informar sobre os diferentes métodos utilizados - intencionalmente ou não -, por médicos, jornalistas, publicitários e etc., para enfatizar opiniões baseando-se em um tratamento matemático tendencioso. Ao informar o leitor, o autor consegue instigar desconfiança e senso crítico, prevenindo-o de aceitar quaisquer informações sem questionamento.
O livro é dividido em 10 capítulos divididos da seguinte forma: os 8 primeiros com críticas específicas, o nono efetivamente uma cartilha resumindo os principais usos dos "métodos" analisados, e o último um guia de prevenção. As críticas são feitas em relação a tendenciosidade da amostra, escolha da média (aritmética, mediana, moda), ocultação de dados, faixa de variação (erro provável e erro padrão), distorção de gráficos, pictogramas tendenciosos, números semiligados, e relações de causa e efeito não provadas.
Algumas das críticas se mostram desatualizadas. O termo "média" é, certamente, menos ambíguo do que nos anos 50, sendo seu uso para média aritmética padronizado em detrimento da média geométrica, moda e mediana. O erro provável descrito no capítulo 4 está em desuso em relação ao erro padrão (também descrito no capítulo, mas em caráter secundário). Porém, algumas críticas feitas continuam muito atuais, por exemplo os gráficos distorcidos e mudança de referencial ao expor percentuais.
[Sobre o Autor e a Escrita]
O autor, como jornalista que era, escreve de maneira fluida e descontraída. A leitura corre naturalmente com a escrita em primeira pessoa, como uma conversa entre amigos. O texto é quase nada técnico e recheado de exemplos fictícios e reais, todos muito bem encaixados com as explicações teóricas. O sucesso do livro reside na escrita pessoal e nas ilustrações.
[Sobre a Formatação e a Edição]
Li a edição em português da Intrínseca. Além do idioma e do acabamento, pouco parece mudar. As ilustrações foram traduzidas quando necessário.
Edição em português da Intrínseca: Como Mentir com Estatística
Recomendado para o público em geral que tem interesse em temas de ciência exatas, mas não gosta de uma profundidade maior de livros como "O Andar do Bêbado" ou "Uma Breve História do Tempo". Mas, dada sua brevidade, o livro pode ser aproveitado, também, pelos leitores de Stephen Hawking e Leonard Mlodinow.
Uma Breve História do Tempo: Uma Breve História do Tempo
O Andar do Bêbado: O Andar do Bêbado. Como O Acaso Determina Nossas Vidas
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The odd-sounding title is easily explained by the author himself. He says he wrote the book much in the same spirit as a burglar might write an instruction manual on how to break into people’s houses — not so much to make it easier for burglars to do so, but so that home-owners can see where their vulnerabilities lie.
These days, the book seems to be even more relevant. Not only are research findings reported in the papers virtually every day, but in education in particular there are quite a few articles of faith that are based on shaky, and sometimes non-existent, foundations.
With chapters like “The well-chosen average”, “The little figures that are not there” and “The semi-attached figure”, the book makes you look at statistics in a different way.
For example, if you were to read a report that tells us that research has shown that 98% of students derive no benefit whatsoever from using technology, you may have a vague feeling of unease about such a finding. However, having read this book you should be able to re-read the report and spot where the statistical sleight of hand occurred (assuming it did occur, of course).
Then again, there are the endless announcements telling us that eating X wards off cancer, causes cancer, is dangerous for people over 40, is only dangerous if you eat more than one a day etc etc ad nauseous. Again, an insight into how some of the figures cited were derived would be immensely helpful in your decision-making.
Illustrated with cartoons by Mel Calman, this light-hearted and slim volume punches way above its weight. Although it was first published over 60 years ago, in 1954, it is still relevant. It should be on every teacher’s shelf and in every school library.
Overall, I found it to be a pleasant and easy-to-read little book about the misuse, either by accident or design, of statistics in everyday life. It's very basic and doesn't go into any detail about more advanced concepts, but it does what it sets out to do very well - and comes across as extremely accessible to readers from any level. Personally, this is a friendly accompaniment to more advanced statistics reading which i'm engaged with, but could also be picked up by someone who desires an awareness of how to avoid being misled by bad statistics.
It is an old book, but it does hold up well in my opinion. Wouldn't hesitate to recommend it.
First, it's about numbers but manages to be both extremely easy to read and very entertaining.
Secondly, although it is so accessible that a ten-year old of average intelligence should be able to understand everything in this book, the points it makes are so universal in application that even someone with much greater mathematical knowledge - and I write this as a graduate with two degrees in a discipline which requires statistical understanding - can find it full of useful reminders and even the odd valuable idea you might not have thought of or heard of.
The book is about how numbers can be manipulated, by accident or design, to trick people into making false conclusions, and how to spot when you are being fed misleading numbers. In this day and age the ability to spot bad statistics is extremely important to everyone and can literally be a life-saver.
I was very surprised indeed to see that a previous reviewer had described this book as "not for everyone." I could not disagree more strongly.
If every voter read this book, fewer bad politicians would be elected on the basis of dishonest campaign statistics, if every consumer read it, fewer bad products would be sold on the basis of dishonest advertising statistics, and if every journalist read it there might be less harm done by scare stories based on bad statistics.
Despite the fact that this book was written many years ago, every single word in it is still very relevant today.
However, anyone with a serious interest in the subject who wants an update on some of the more recent examples of how statistics are misused should still start by reading "How to Lie with Statistics" and then follow up with the equally good "Damn Lies and Statistics" by Joel Best, which is more current and nearly as accessible. The two books complement each other very well.
If you spotted the fast one I pulled in the first paragraph, you're either one of "the crooks" who already know these tricks or else are an honest soul who has learned them "in self-defence". Hence the title of this fantastic little book: knowing how a burglar thinks helps secure your house. Most of the time, I would pass over the phrase "average wage" without a second glance. We all know what an average is, don't we? Distant maths lessons are just that for most of us, and even if I'd dredged up the question - what kind of average? - would I have been bothered to ask it? Complacency translates into vulnerability.
"When you are told that something is an average you still don't know very much about it unless you can find out which of the common kinds of average it is - mean, median, or mode." Without a clear understanding of these different kinds of average, you have to hope it doesn't really matter which one is being used, but this is only the case "when you deal with data... that have the grace to fall close to what is called the normal distribution." Otherwise, it makes a big difference, so much so that, "as usually is true with income figures, an unqualified 'average' is virtually meaningless."
Advertisers, of course, are among the most culpable and capable when it comes to lying with statistics (although at least their motives are plain). It is typical of Huff's sense of mischief that, alongside the calculations, he presents us with an ethical dilemma of enormous proportions: should we feel sorry for advertisers who are themselves victims of statistical skulduggery? For example, a magazine publisher is happy to state the median age of its readership, while leaving the kind of average for incomes "carefully unspecified". "Could it be that the mean was used instead because it is bigger, thus seeming to dangle a richer readership before advertisers?"
This is a short book, made even shorter by pictures of cows and charts that take up half a page. (How the innocent-looking graph can be manipulated by adding "schmaltz" is another example of Huff's style: a simple unpicking of the familiar to demonstrate an important point.) It is also unreasonably funny in parts. I don't recall maths, let alone statistics, ever being this entertaining at school. And yet the intellectual content is not compromised. Huff's message is a serious one and perhaps more important now, since our propensity for attaching numbers to almost anything shows no sign of diminishing. It ought to be common knowledge that samples can be "biased by the method of selection", that "well-biased samples can be employed to produce almost any result anyone may wish", that it can be difficult to obtain "a representative sample... one from which every source of bias has been removed", that people who answer survey questions have "a desire to give a pleasing answer", that strange results "crop up when figures are based on what people say".
Most of us can understand these ideas when they are explained by someone like Huff (although it might help not to be an aristocrat). If we're honest, out in the wild without a guide, we're not so sure. Have you ever been scared by "accident statistics"? Would the fact that more people "were killed by aeroplanes last year than in 1910" give you pause for thought? Are modern planes really more dangerous? "Nonsense. There are hundreds of times more people flying now, that's all."
"It is sometimes a substantial service simply to point out that a subject in controversy is not as open-and-shut as it has been made to seem." Or, as Goldacre's slogan has it: "I think you'll find it's a bit more complicated than that..." Both belong to that noble tradition of satire with a serious message, and it is a tribute to Huff's writing style that he can end with a quote from Mark Twain that fits perfectly: "There is something fascinating about science. One gets such wholesale returns of conjecture out of such a trifling investment of fact."
The book has plenty of relevant examples to illustrate the points, and while some will be dated (doctors advertising tobacco), the general principles behind them are as valid today as they were when the book was first written.
On top of that the book contains some hilarious illustrations by Mel Calman, which lighten the mood and make it a pleasure to read.
The book is not a comprehensive guide to statistics, innumeracy or graphical (mis)representation of data but it is a good starting point for all of these. If it strikes a chord, I would also warmly recommend Innumeracy: Mathematical Illiteracy and Its Consequences by John Allen Paulos and Tufte's The Visual Display of Quantitative Information, as well as How to Measure Anything: Finding the Value of Intangibles in Business by Douglas Hubbard.
There is a more modern book by Alberto Cairo called "How Charts Lie" which I would recommend you to read as well for a more modern take.
Buy it for the cartoon illustrations. Read it for the knowledge.
If you have heard of all the above then I think you know enough about statistics to know what this book will entail.
An enjoyable read with a good writing style. Just not the statistical nirvana its made out to be.
Well in fact that's not right. This slim and well written book will ensure you ask the questions of any data in front of you, to ask if it makes sense, is correct and can be trusted.
Recommended for data sceptics everywhere.
I enjoyed reading it again - now so that I did not lie with Stats - but how I can learn agin just how easily statistics can be bent to
"prove" a case.
I have written a blog modelling the ideas from the book but relating it to risks: How To Lie With Risks. [...]