What do we see when we look at slices of a pie chart? Angles? Areas? Arc length? The answer to this question isn’t clear and thus “experts” recommend avoiding pie charts at all.
Robert Kosara is a Senior Research Scientist at Tableau Software (you should follow his blog https://eagereyes.org), who is very active in studying pie charts. In 2016, Robert Kosara and his collaborators published a series of studies about pie charts. There is a nice post called “An Illustrated Tour of the Pie Chart Study Results” that summarizes these studies.
Last week, Robert published another paper with a pretty confident title (“Evidence for Area as the Primary Visual Cue in Pie Charts”) and a very inconclusive conclusion
While this study suggests that the charts are read by area, itis not conclusive. In particular, the possibility of pie chart usersre-projecting the chart to read them cannot be ruled out. Furtherexperiments are therefore needed to zero in on the exact mechanismby which this common chart type is read.
Kosara. “Evidence for Area as the Primary Visual Cue in Pie Charts.” OSF, 17 Oct. 2019. Web.
The previous Kosara’s studies had strong practical implications, the most important being that pie charts are not evil provided they are done correctly. However, I’m not sure what I can take from this one. As far as I understand the data, the answer to the questions in the beginning of this post are still unclear. Maybe, the “real answer” to these questions is “a combination of thereof”.
Data-ink ratio is considered to be THE guiding principle in data visualization. Coined by Edward Tufte, data-ink is “the non-erasable core of a graphic, the non-redundant ink arranged in response to variation in the numbers represented.” According to Tufte, the ratio of the data-ink out of all the “ink” in a graph should be as high as possible, preferably, 100%.
Everyone who considers themselves serious about data visualization knows (either formally, or intuitively) about the importance to keep the data-ink ratio high, the merits of high signal-to-noise ratio, the need to keep the “chart junk” out. Everybody knows it. But are there any empirical studies that corroborate this “knowledge”? One of such studies was published in 1988 by James D. Kelly in a report titled “The Data-Ink Ratio and Accuracy of Information Derived from Newspaper Graphs: An Experimental Test of the Theory.”
In the study presented by J.D. Kelly, the researchers presented a series of newspaper graphs to a group of volunteers. The participants had to look at the graphs and answer questions. A different group of participants was exposed to similar graphs that underwent rigorous removal of all the possible “chart junk.” One such an example is shown below
Unexpectedly enough, there was no difference between the error rate the two groups made. “Statistical analysis of results showed that control groups and treatment groups made a nearly identical number of errors. Further examination of the results indicated that no single graph produced a significant difference between the control and treatment conditions.”
I don’t remember how this report got into my “to read” folder. I am amazed I have never heard about it. So, what is my take out of this study? It doesn’t mean we need to abandon the data-ink ratio at all. It does not provide an excuse to add “chart junk” to your charts “just because”. It does, however, show that maximizing the data-ink ratio shouldn’t be followed zealously as a religious rule. The maximum data-ink ratio isn’t a goal, but rather a tool. Like any tool, it has some limitations. Edward Tufte said, “Above all, show data.” My advice is “Show data, enough data, and mostly data.” Your goal is to convey a message, if some decoration (a.k.a chart junk) makes your message more easily digestible, so be it.
You know that I like reading a ruthless critique of others’ work — I like telling myself that by doing so I learn good practices (in reality, I suspect I’m just a case what we call in Hebrew שמחה לאיד — the joy of some else’s failure).
Anyhow, I’d like to share a set of posts by Lior Patcher in which he calls bullshit on several reputable people and concepts. Calling bullshit is easy. Doing so with arguments is not so. Lior Patcher worked hard to justify his opinion.
Unfortunately, I don’t publish academic papers. But if I do, I will definitely want prof. Patcher read it, and let the world know what he thinks about it. For good and for bad.
Speaking of calling bullshit. Believe it or not, University of Washington has a course with this exact title. The course is available online http://callingbullshit.org/ and is worth watching. I watched all the course’s videos during my last flight from Canada to Israel. The featured image of this post is a screenshot of this course’s homepage.
From time to time, you might hear that graphical method A is better at representing problem X than method B. While in case of problem Z, the method B is much better than A, but C is also a possibility. Did you ever ask yourselves (or the people who tell you that) “Says WHO?”
It looks like a very interesting and relevant paper, despite the fact that it has been it was published 22 years go. I will certainly read it. Following is the reading list that I compiled for my data visualization students more than two years ago. Unfortunately, they didn’t want to read any of these papers. Maybe some of the readers of this blog will …
Как это не печально, большинство начинающих блоггеров забрасывают свой блог вскоре после его открытия. Что отличает успешных (стойких?) блоггеров от тех, которым не удаётся продержаться? Стоит ли вести коллективные блоги, и если да, как важно распределение труда между авторами?
В этой лекции мы попытаемся пролить свет на эти вопросы, анализируя поведение более пяти миллионов пользователей WordPress.com.