Simplicity in Graphical Representation: 10 Quotes
“Graphic charts have often been thought to be tools of those alone who are highly skilled in mathematics, but one needs to have a knowledge of only eighth-grade arithmetic to use intelligently even the logarithmic or ratio chart, which is considered so difficult by those unfamiliar with it. […] If graphic methods are to be most effective, those who are unfamiliar with charts must give some attention to their fundamental structure. Even simple charts may be misinterpreted unless they are thoroughly understood. For instance, one is not likely to read an arithmetic chart correctly unless he also appreciates the significance of a logarithmic chart.” (John R Riggleman & Ira N Frisbee, “Business Statistics”, 1938)
“In many instances, a picture is indeed worth a thousand words. To make this true in more diverse circumstances, much more creative effort is needed to pictorialize the output from data analysis. Naive pictures are often extremely helpful, but more sophisticated pictures can be both simple and even more informative.” (John W Tukey & Martin B Wilk, “Data Analysis and Statistics: An Expository Overview”, 1966)
“Pencil and paper for construction of distributions, scatter diagrams, and run-charts to compare small groups and to detect trends are more efficient methods of estimation than statistical inference that depends on variances and standard errors, as the simple techniques preserve the information in the original data.” (W Edwards Deming, “On Probability as Basis for Action”, American Statistician Vol. 29 (4), 1975)
“The more complex the shape of any object, the more difficult it is to perceive it. The nature of thought based on the visual apprehension of objective forms suggests, therefore, the necessity to keep all graphics as simple as possible. Otherwise, their meaning will be lost or ambiguous, and the ability to convey the intended information and to persuade will be inhibited.” (Robert Lefferts, “Elements of Graphics: How to prepare charts and graphs for effective reports”, 1981)
“The truth is that one display is better than another if it leads to more understanding. Often a simpler display, one that tries to accomplish less at one time, succeeds in conveying more insight. In order to understand complicated or subtle structure in the data we should be prepared to look at complicated displays when necessary, but to see any particular type of structure we should use the simplest display that shows it.” (John M Chambers et al, “Graphical Methods for Data Analysis”, 1983)
“What about confusing clutter? Information overload? Doesn’t data have to be ‘boiled down’ and ‘simplified’? These common questions miss the point, for the quantity of detail is an issue completely separate from the difficulty of reading. Clutter and confusion are failures of design, not attributes of information.” (Edward R Tufte, “Envisioning Information”, 1990)
“Graphical illustrations should be simple and pleasing to the eye, but the presentation must remain scientific. In other words, we want to avoid those graphical features that are purely decorative while keeping a critical eye open for opportunities to enhance the scientific inference we expect from the reader. A good graphical design should maximize the proportion of the ink used for communicating scientific information in the overall display.” (Phillip I Good & James W Hardin, “Common Errors in Statistics (and How to Avoid Them)”, 2003)
“A common mistake is that all visualization must be simple, but this skips a step. You should actually design graphics that lend clarity, and that clarity can make a chart ‘simple’ to read. However, sometimes a dataset is complex, so the visualization must be complex. The visualization might still work if it provides useful insights that you wouldn’t get from a spreadsheet. […] Sometimes a table is better. Sometimes it’s better to show numbers instead of abstract them with shapes. Sometimes you have a lot of data, and it makes more sense to visualize a simple aggregate than it does to show every data point.” (Nathan Yau, “Data Points: Visualization That Means Something”, 2013)
“The biggest thing to know is that data visualization is hard. Really difficult to pull off well. It requires harmonization of several skills sets and ways of thinking: conceptual, analytic, statistical, graphic design, programmatic, interface-design, story-telling, journalism — plus a bit of ‘gut feel.’ The end result is often simple and beautiful, but the process itself is usually challenging and messy.” (David McCandless, 2013)
“Dashboards are collections of several linked visualizations all in one place. The idea is very popular as part of business intelligence: having current data on activity summarized and presented all inone place. One danger of cramming a lot of disparate information into one place is that you will quickly hit information overload. Interactivity and small multiples are definitely worth considering as ways of simplifying the information a reader has to digest in a dashboard. As with so many other visualizations, layering the detail for different readers is valuable.” (Robert Grant, “Data Visualization: Charts, Maps and Interactive Graphics”, 2019)
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