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====== Data Visualization ======

//{{tag>Emerson Tufte}}//

At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numbers—even a very large set of numbers—is to look at pictures of those numbers. Furthermore, of all methods for analyzing and communicating statistical information, well-designed data graphics are usually the simplest and at the same time the most powerful.
\\ **\cite[p.9]{VisualDisplayQI}**

Excellence in statistical graphics consists of complex ideas communicated with clarity, precision, and efficiency.
\\ **\cite[p.13]{VisualDisplayQI}**

Tables usually outperform graphics in reporting small data sets of 20 numbers or less. The special power of graphics comes in the display of large data sets.
\\ **\cite[p.56]{VisualDisplayQI}**

Each part of a graphic generates visual expectations about its other parts and, in the economy of graphical perception, these expectations often determine what the eye sees. Deception results from the incorrect extrapolation of visual expectations generated at one place on the graphic to other places.
\\ **\cite[p.60]{VisualDisplayQI}**

A steady canvas makes for a clearer picture.
\\ **\cite[p.61]{VisualDisplayQI}**

Above all else show the data.
\\ **\cite[p.92]{VisualDisplayQI}**

What is to be sought in designs for the display of information is the clear portrayal of complexity.
\\ **\cite[p.191]{VisualDisplayQI}**

Translating data into a visual format may help reveal patterns that might not otherwise be apparent. Representing data visually on a chart or graph can reveal wider trends and unexpected clusters around specific demographics, geographies or time-periods.
\\ **\cite[p.11]{ID4Advocacy}**

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