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Visual Display of Quantitative Information
Statistical graphics provide a way for researchers to present complex information from a data set that may not be easily conveyed by words alone. Edward Tufte, a statistician and artist, devoted his career to the field of statistical graphics. Originally trained as a political scientist at Yale University, Tufte eventually became a professor at his alma mater and taught courses on data analysis and graphics. In 1983, he published the influential book The Visual Display of Quantitative Information. This book focuses on principles to create elegant, informative graphics and also provides the reader with ample examples of both good graphics and graphical elements to avoid. Tufte’s book provides researchers with tools to display data and findings in a thoughtful and clear manner.
History and Graphical Deception
The Visual Display of Quantitative Information begins with a review of the history of graphics and credits the works of Johann Hinrich Lambert and William Playfair in the 1700s as ushering in the era of modern graphics. Tufte notes that the goal of early graphics was to communicate interpretations of a data set, but that around the 1900s the use of statistical graphics shifted toward focusing more on combatting deception than analyzing data. As an effect, graphics became simply a way to display what was obvious about the data—that is, until the introduction of John Tukey’s graphics in the 1960s.
When creating a graphic, Tufte acknowledges the importance of a graphic’s lie factor, which can be calculated in the following manner:
Lie factor = Size of the effect shown in the
graphic / Size of the effect in the data itself.
The lie factor should be around 1 to accurately convey the data to the reader (i.e., the graphic should mirror the size of the effect found in the data, not belittle or exaggerate it). Of note, overdecoration of a graphic, such as portraying bars in 3D when the data vary in two dimensions, should be avoided as it can create graphical deception. However, enough information should be provided to put the data in context and ensure nothing is missing.
The Importance of Ink in a Graphic
The remainder of the book focuses on principles and techniques to follow when creating good statistical graphics, the most important of which is, “Above all else, show the data.” One way to ensure this principle is followed is by paying attention to the ink used in the creation of the graphic. Specifically, Tufte suggests aiming for a large data–ink ratio, which is calculated:
(Data − ink) ratio = Data − ink /
Total ink used in the graphic.
Paying attention to a graphic’s data–ink (ink used to convey the data) ratio can help limit non-data–ink (ink used for purposes other than displaying data) and redundant data–ink (ink used to show the same information in multiple ways). Oftentimes, these types of ink can be erased without losing critical information. For example, a bar chart that is both shaded and labeled has quite a bit of redundant data–ink which can be erased and still convey the data. Decorations are also considered non-data–ink or redundant data–ink and are called chartjunk by Tufte. Grids, coordinate lines, frames, and tick marks are common elements of chartjunk that can either be fully or partially removed without changing the graphic’s interpretation. In fact, removing chartjunk can increase the ease of interpretation by limiting clutter that might obscure the data.
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