Personal Notes on Edward R. Tufte: The Visual Display of Quantitative Information

Reference:
Tufte, Edward R. The Visual Display of Quantitative Information, Graphics Press, Cheshire, Connecticut, 1989.

Introduction
Data graphics visually display measured quantities by means of the combined use of points, lines, a coordinate system, numbers, symbols, words, shading and color.

The use of abstract, non-representational pictures to show numbers is a surprisingly recent invention, perhaps because of the diversity of skills required--the visual-artistic, empirical-statistical, and mathematical. It was not until 1750-1800 that statistical graphics--length and area to show quantity, time-series, scatterplots, and multivariate displays--were invented, long after such triumphs of mathematical ingenuity as logarithms, Cartesian coordinates, the calculus, and the basics of probability theory. The remarkable Willial Playfair (1759-1823) developed or improved upon nearly all the fundamental graphical designs, seeking to replace conventional tables of numbers with the systemtatic visual representations of his "linear arithmetic".

Modern data graphics can do much more than simply substitute for small statistical tables. At their best, graphics are instruments for reasoning about quantitative informaton. Often the most effective way to describe, explore, and summarize a set of umbers--even a very large set--is to look at pictures of those numbers. Furthermore, of all methods for analyzing and communicating statistical informaton, well-designed data graphics are usually the simplest and at the same time the most powerful.

I. Graphical Excellence
Excellence in statistical graphics consists of complex ideas communicated with clarity precision, and efficiency. Graphical displalys should:

• show the data

• induce the viewer to think about the stustance rather than about methodology, graphic design, the technology of graphic production, or something else

• avoid distorting what the data have to say

• present many numbers in a small space

• make large data sets coherent

• encourage the eye to compare different pieces of data

• reveal the data at several levels of detail, from a broad overview to the fine structure

• serve a reasonably clear purpose: description, exploration, tabulation, or decoration

• be closely integrated with the statistical and verbal descriptions of a data set
Graphics reveal data. Indeed graphics can be more precise and revealing than conventional statistical computations. Consider Anscombe's quartet: all four of these data sets are described by exactly the same linear model (at least until the residuals are examined).

```--------------------------->TABLE HERE<----------------------

-------------------------->FIGURE HERE<---------------------

F.J. Anscombe, "Graphs in Statistical Analysis," American Statistician, 27
(February 1973), 17-21.

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Personal Notes of
Ronald D. Kriz
Virginia Tech
Revised November 23, 1995