The same dataset, visualized two different ways. The left fixates on between-group differences, which can encourage stereotyping. The right shows both between and within group differences, which may discourage viewers’ tendencies to stereotype the groups being visualized.
Excerpt:
Ignoring or deemphasizing uncertainty in dataviz can create false impressions of group homogeneity (low outcome variance). If stereotypes stem from false impressions of group homogeneity, then the way visualizations represent uncertainty (or choose to ignore it) could exacerbate these false impressions of homogeneity and mislead viewers toward stereotyping.
If this is the case, then social-outcome-disparity visualizations that hide within-group variability (e.g. a bar chart without error bars) would elicit more harmful stereotyping than visualizations that emphasize within-group variance (e.g. a jitter plot).
On page 166 of her 1952 book, in a chapter titled “The Bar Chart”, Spear shows very clearly an early form of a chart type called the Box Plot that she calls the “Range Bar.” …..
What’s interesting about this to me is that if you look up the Wikipedia page for Box Plot, at the present moment, you will not find Spear’s name appearing anywhere in the article. You will, however, read the following:
“Since the mathematician John W. Tukey introduced this type of visual data display in 1969, several variations on the traditional box plot have been described.”
The way I see it, the range bar appearing in Spear’s book is close enough in form to the box plot to warrant a mention on this Wikipedia page. Hopefully, by the time you read this, you’ll be able to find an updated page for the box plot with her name included on it.
Data visualizations and graphs are increasingly common in both scientific and mass media settings. While graphs are useful tools for communicating patterns in data, they also have the potential to mislead viewers. In five studies, we provide empirical evidence that y-axis truncation leads viewers to perceive illustrated differences as larger (i.e., a truncation effect). This effect persisted after viewers were taught about the effects of y-axis truncation and was robust across participants, with 83.5% of participants across these 5 studies showing a truncation effect. We also found that individual differences in graph literacy failed to predict the size of individuals’ truncation effects. PhD students in both quantitative fields and the humanities were susceptible to the truncation effect, but quantitative PhD students were slightly more resistant when no warning about truncated axes was provided. We discuss the implications of these results for the underlying mechanisms and make practical recommendations for training critical consumers and creators of graphs.
Author(s): Brenda W. Yang, Camila Vargas Restrepo, Matthew L. Stanley, Elizabeth J. Marsha
Publication Date: 16 February 2021
Publication Site: Journal of Applied Research in Memory and Cognition
For this specific application, when I think of lawfulness, I am going to mainly assess the likelihood to be misused. And for good versus evil, I’ll be looking at how well they can typically help the user understand the data.
Lawful Good: Bar Chart
This is the best alignment you can be. In traditional use, lawful good applies to people that both follow the rules and help others. Here I’m applying it to a chart that I think is often used well and is easy to read. Name a better liked and more used chart than the bar chart – you can’t. 10/10 analysts would recommend.
