Data Visualization

Data is constantly being visualized in the world around us; some in clear and accurate ways, while other visualizations are muddy and deceptive. Being information literate means being able to recognize when information is being dishonestly displayed and not falling into its trap of deceiving the mind. In class we analyzed what can make certain data visualizations deceptive by reading Irving Geis and Darrel Huff’s text, How to Lie with Statistics.

Huff and Irving use mini comics to display how certain modifications to data visualizations like graphs and charts can distort the reader’s perception of the information and talk about how people can be misled by simple statistical reports as well. One of the things that Huff and Irving warn readers of is the population sample bias which exists when people are collecting information for a poll and selectively choosing their participants rather than using fair, diverse data collection methods. They also talk about how people are tricked by deceptive comparisons, the use of misleading base rates in graphs, and distorted object sizes in visualizations.

Being able to recognize the tactics that misleading data visualizations use is a very valuable skill. In Anshul Vikram Pandey, Katharina Rall, Margaret L. Satterthwaite, Oded Nov, and Enrico Bertini’s research project “How Deceptive Are Deceptive Visualizations?: An Empirical Analysis of Common Distortion Techniques” published by a reputable publisher, they show just how deceptive the information distortion tricks that Huff and Irving talk about truly are. Pandey and his team presented control graphs and similar graphs that were modified using popular information distortion methods to an audience and then asked them questions about the information being represented on those graphs. For the normal graphs, their participants had an error rate of about 14% when answering the team’s questions, but for the distorted graphs, the participants had an outstanding error rate of 97.5%. Also, when asked to guess the size of data being represented by a certain graph meant to make the data look bigger than it really is, the participants guessed a size of the data that was 60–130% larger than the actual value.

Pandey and the rest of his team’s study highlights how vulnerable the average population is to being victimized by the deceptive data portrayal that Gilmore brings awareness to. Pandey’s research report shows another reason why it is important to be information literate. Without information literacy, and the ability to recognize deceptive information, people can be taken advantage of and have their views morphed by insidious people or things.

Work Cited

Huff, Darrel, and Irving Geis. How to Lie with Statistics. New York: Norton, 1993. Print.

Pandey, Anshul Vikram, Katharina Rall, Margaret L. Satterthwaite, Oded Nov, and Enrico Bertini. “How Deceptive Are Deceptive Visualizations?: An Empirical Analysis of Common Distortion Techniques.” Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems — CHI ’15 (2015): n. pag. Web. 10 Dec. 2016.