The illusion knowing #data_visualization by intuition.
I used to think that I intuitively know "data visualization", as I was a hashtag #statistics instructor at a prestigious university, and #data_scientist in industry. Anyhow, I knew how to make graphs and charts out of quantitative and qualitative variables using #R or #Tableau!
This illusion of #visual_literacy fell apart with my first encounter with "Information Visualization" courses, textbooks, and seminal works. I figured out that possibly every plot I produce as "data visualization", is not the best alternative in the solution space. InfoVis, beside theory, has a long history of lab work. From the seminal work of Cleveland and McGill(1984) to Heer and Bostock(2010), effectiveness of different visual channels, such as position or angle, are examined by InfoVis labs or crowdsourcing. What I did not know "intuitively" was the "objective" measures of evaluation of plots. Some plots are better than others, "objectively". In other words, our brains are wired to perceive some plots more accurately than other plots.
Below are two figures to summarise some of this objectivity, based on the error of experiment participants perception error. These are the lest in hashtag#InfoVis, and not knowing them make us to somehow "visually illiterate". (The figure from (Heer and McGill,2010))
I will write more regularly on this topic.
Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American statistical association, 79(387), 531-554.
Heer, J., & Bostock, M. (2010, April). Crowdsourcing graphical perception: using mechanical turk to assess visualization design. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 203-212). ACM.