visual displays of quantitative information

VISUAL DISPLAYS OF QUANTITATIVE INFORMATION HOW TO

Write out explainations of the data on the graphic.

Clear, detailed, and thorough labeling should be used to defeat graphical distortion and ambiguity.

The representation of numbers, as physically measured on the surface of the graphic itself, show be directly proportional to the nubmer of guantities represented.

Graphical integrity is more likely to result if these six principles are followed: Tufte sets out principles to avoid such distortions with the following guidelines: One can lie with graphics by changing scales so correct comparisons are hard or impossible ( Lie factor, design variation, not data variation, lack of data standardization), one can trick the reader through abusing visual expectations ( changes in multiple dimensions for the same variable), and one can confuse the reader with poor labeling and ambiguity in presentation.

Don’t Lie / Distort the Data / Mislead the Readerĭistortion in graphics comes in many shapes and sizes. This blog post will be serving as a dumping ground for thoughts on the text, including graphics and potential sketches of extensions.ġ.

VISUAL DISPLAYS OF QUANTITATIVE INFORMATION HOW TO

What it means to create an amazing graphic, and how to do so within your texts and full presentations.īelow I’ve summarized these sections.Create graphics that showcase complex ideas and patterns (Associated with data density) with encouragement of multiple graphics in 1.Provide a clean graphic (avoid distractions) and avoid excessive unnecessary non-data-ink, also leverage multifunctionality of graphic display elements.Don’t Lie / Distort the Data / Mislead the Reader with your graphics (Associated with lie factor).Now that I’ve read his text I have a much larger appreciation for this thoughts. That means it may be less of book report / review as a summary highlight points I found most relevant.īefore reading Tufte’s book “The Visual Display of Quantitative Information”, I always saw Tufte as someone that presented views on graphics that were a little too extreme for my taste, largely a thinker reacting to horrible graphics of the time, and not a thinker that would be remembered for much beyond that. I suspect they will be little more than detailed book reports, but will also contain thoughts of mine of relative examples and will focus on formatting the ideas for students of 36-315, a course taught by the Statistics and Data Science Department at Carnegie Mellon University. The blog post will be one of a few posts that focus on Graphical Theory based on some standard texts in the area. On March 5, 2010, President Barack Obama appointed Tufte to the American Recovery and Reinvestment Act's Recovery Independent Advisory Panel 'to provide transparency in the use of Recovery-related funds'.Review of Tufte’s 'The Visual Display of Quantitative Information' 6 Aesthetics and data graphical displays.Ībout the author Edward R.Tufte is an American statistician and professor emeritus of political science, statistics, and computer science at Yale University ( Source: Wikipedia). data variation, and sources of deception. Detection of graphical deception: design variation vs. Time-series, relational graphics, data maps, multivariate designs. Some topics that the book dives deep into are: Design of the high-resolution displays, small multiples. It encompasses both theory and practice in the design of data graphics including 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. This is a classic book on statistical graphics, charts, tables.

Visual displays of quantitative information