- Gebundene Ausgabe: 197 Seiten
- Verlag: Bertrams; Auflage: 2 (31. Januar 2001)
- Sprache: Englisch
- ISBN-10: 0961392142
- ISBN-13: 978-0961392147
- Größe und/oder Gewicht: 2,5 x 22,9 x 27,9 cm
- Durchschnittliche Kundenbewertung: 2 Kundenrezensionen
- Amazon Bestseller-Rang: Nr. 5.737 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Visual Display of Quantitative Information (Englisch) Gebundene Ausgabe – 31. Januar 2001
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The first edition of Tufte's now classic text on the design of statistical graphics was published in 1983. Tufte published it himself with the help of a second mortgage in order to have complete control over the book's design, which he wanted to reflect the intellectual principles put forth in its c
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Tufte is an eminent expert on the topic and he presents his case clear and with passion. Furthermore the book is simply stunningly beautiful.
there are some nice historical examples of how _not_ to do it, and of how the displaying of information was always used as a means for manipulating the viewer, plus some easy 1-2-3 guidelines for avoiding the pitfalls of manipulation and bad design, BUT this mostly applies for histograms and 2D plots. Newer formats, including color, 3D, video and contour-plots are passingly mentioned, without guidelines, opinions or any type of helpful comment.
the picture on the cover (a brilliantly displayed train schedule) says it all: look at this piece of specialized display, realize it is great, look around you, realize how many bad data-displays there are, and now think of you own, specialized way of displaying your own, specialized data.
there is no spoon.
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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. Which goes to show -- once again -- "a picture is worth a thousand words." Sadly, the illustration on page 140 is small and in black and white. I wish the second edition included a larger reproduction of this photo. A color presentation would have been helpful.
Second, Tufte continues his unrelenting pattern to reinforce the importance and impact of illustrations in understanding complex concepts. In particular, page 176 demonstrates the impact of Napoleon's march to Moscow. The illustration is both profound and eerie. The reader is left with a feeling of death and pain for the foot soldiers...
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). Good examples include: samples versus populations, continuous versus categorical data, and exploratory graphics versus graphics presented for presentation.
For that reason, the academic and statistician in me is watchful of Tufte's role as an instructor of statistical ideas. Much of what Tufte has to say is not in fact unique or necessarily "right," and also not nearly close to being all there is to be said about statistical graphics (even at an introductory level). If students allow this text to be the sole contribution to their statistical education, I fear that -- without statistical intuition or knowledge to draw from -- they will not be critical statistical thinkers but blind followers. (Of course, none of this is intended to be a criticism of Tufte or Tufte's book.)
Those seeking a good overview of statistical graphics: keep in mind that this not strictly an instructional book. And while I wouldn't discourage you from reading or buying this text, I also wouldn't discourage you from seeking additional resources, either as an alternative or a supplement to Tufte's works. Much of the ideas supplied by Tufte here -- plus a great deal more -- can fundamentally be found in a good introductory statistical course or text, either directly or indirectly. Moreover, I would argue that there is absolutely no substitution for such an education.
Tufte starts with a simple proposition: graphs and graphics
that represent statistical data should tell the truth. It's
amazing how often designers of such graphics miss this basic
point. Tufte clearly and entertainingly elucidates the most
common "graphical lies" and how to avoid them.
book and you'll never look at a newspaper or presentation
graphics the same way again -- you'll be left wondering if
the author *intended* to lie about what the data were saying, or if he/she just didn't know any better.
Another reviewer claimed that this book talks about how to make graphics accurate, not beautiful. He's right in some sense, but who cares? There are a million books on how to make "pretty" graphical displays, but precious few on how to make useful ones. These books are they.
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