- Hardcover: 200 pages
- Publisher: Graphics Pr; 2nd edition (January 2001)
- Language: English
- ISBN-10: 0961392142
- ISBN-13: 978-0961392147
- Product Dimensions: 1 x 9 x 11 inches
- Shipping Weight: 2.5 pounds (View shipping rates and policies)
- Average Customer Review: 4.4 out of 5 stars See all reviews (241 customer reviews)
- Amazon Best Sellers Rank: #5,787 in Books (See Top 100 in Books)
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The Visual Display of Quantitative Information 2nd Edition
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Top Customer Reviews
Good graphic design, he argues, reveals the greatest number of ideas in the shortest time with the least ink in the smallest space. Interestingly, some of the best examples of this come from the pre-computer era, when graphics had to be drawn by hand (and therefore more thought had to go into their design, rather than the author just calling up the Bar Graph template on the desktop.) For example, that picture you can see on the front cover of the book is actually a train timetable that packs a whole list of arrivals and departures at many different stations into a single little picture. A better example (and the "best statistical graphic ever drawn") shows Napoleon's route through Europe. It shows a) the map b) where he went c) how many people were in his army at each point and d) the temperature on the way back that killed off his army. At a glance you can see the factors that led to his army losing. AND it was drawn by hand in 1885 and is little more than a line drawing!
He also gives examples of really bad design, (including "the worst graphic ever to make it to print"), and shows what makes it so bad. His examples prove that information-less, counter-intuitive graphics can still look dazzlingly pretty, even though they're useless. In some examples, he shows how small changes can make the difference between an awful graphic and a really good one. My favourite example of this is how he drew the inter-quartile ranges on the x and y axes of a scatterplot, thus adding more information to the graphic without cluttering it up.
In summary, there's a lot more to good graphic design than being an Adobe guru. Reading this book made me feel like a more discerning viewer of graphics!
Edward R. Tufte is a noteworthy scholar and the presentation of the material presented in this book is awe-inspiring. Tufte has also compiled two other books that can be best described as quite remarkable. These additional books are entitled, ENVISIONING INFORMATION and VISUAL EXPLANATIONS. All three of these volumes are not merely supplemental textbooks; they are works of art.
My intent was to use VISUAL DISPLAY OF QUANTITATIVE INFORMATION as part of teaching my statistics course. Students, but mostly faculty, are overly impressed with inferential statistics. Graphics play an important role in the understanding and interpretation of statistical findings. Tufte makes this point unambiguously clear in his books.
Two features of VISUAL DISPLAY OF QUANTITATIVE INFORMATION are particularly salient in teaching a statistics course. First, the concept of normal distribution is wonderfully illustrated on page 140. Here the reader is reinforced with the notion that in the normal course of human events, cultural/social/behavioral/ psychological phenomena usually fall into the shape of a normal distribution. The constant appearance of this distribution borders on miraculous. Just as importantly, it is the basis for accurate predications in all areas of science. Tufte's illustration (page 140) speaks to this issue much more clearly than a one-hour lecture on the importance of the normal distribution.Read more ›
As a graphic designer and a minimalist, I love the way this book looks and I love the graphics Tufte's team has created.
Yet, the minimalist in me also dislikes Tufte's prose, which is surprisingly un-minimalist. The text is repetitive, and although Tufte does use this effectively at times to reiterate or summarize concepts, there are far more instances where I feel the repetition is simply irritating (Tufte's poems and block-quote summaries are, to me, good examples of this).
The minimalist in me is also not fond of the nature in which Tufte presents his opinions. Tufte makes frequent use of words like "lies" and "tricks," and while I am not fond of the targets of Tufte's derision, I feel that use of these words unnecessarily and unfairly assumes that poor graphs are always the result of malicious intent. Tufte's presentation as a whole, I feel, is often unnecessarily condescending (see e.g., p 120); indeed, Tufte seems to feel that unenlightened minds somehow deserve our ridicule and contempt.
As an academically oriented statistician, I also have mixed feelings. I give Tufte an immense amount of credit for opening a dialog about statistical graphics. And, I am grateful to him for pointing out the flaws and "wrongs" in the ways in which statistics are so often presented and suggesting ways in which these approaches can be changed. Moreover, I happen to agree tremendously with a large amount of what Tufte has to say, and often passionately so.
That said, I am puzzled by the amount of relevant concepts which are omitted from this text (or merely brushed over).Read more ›
Most Recent Customer Reviews
Tufte has written several outstanding books. I bought this copy for our investment advisors because they, like so many people, were misusing graphs. Read morePublished 6 days ago by Skeptic
To me, Tufte's series of books on data visualization seem like the explorations of somebody who is beginning a new branch of science. Read morePublished 1 month ago by Aran Joseph Canes
Edward Tufte literally wrote the book on data visualization setting off a growing trend in beautiful and informative infographics. Highly recommend.Published 2 months ago by John S Nee
Very needed book for scientists, engineers and anyone presenting data.
Great examples and easy to read or skip around.
Incredible book, highly recommend for anyone pursuing a graduate degree in engineering.Published 3 months ago by Grad Student
Tufte makes some reasonable points, including pointing out ways that graphics manipulate and exaggerate data. Read morePublished 3 months ago by Daniel M.