Data visualization a powerful tool for understanding your data and telling stories about it to others. Exploratory visualization can help you discover patterns in the data that may not be clear from statistics alone. A good example is Anscombe's quartet below, which shows how data with the same summary statistics (e.g., mean, standard distribution) can actually look very different and tell very different stories! Alberto Cairo updated Anscombe's idea by creating the Datasaurus dataset below, which urges people to "never trust summary statistics alone; always visualize your data." These examples rebut the notion that "charts are simply 'pretty pictures,' while all of the important information can be divined through statistical analysis" (Matejka and Fitzmaurice).
In addition to supporting a deeper understanding of data patterns, visualization is also a frequent final output of research whether for scholarly papers, posters, or grant proposals. Putting some time and thought into data visualization upfront can help you create more effective charts, graphs, and figures.
While software and data type may vary by discipline, the underlying concepts of good data visualization are consistent. This guide provides guidance on best practices and useful tools to help guide successful data visualization.
"All four sets are identical when examined using simple summary statistics, but vary considerably when graphed."
This guide is adapted with permission from the University of San Diego by Stephanie Labou.
Wikipedia contributors, "Anscombe's quartet," Wikipedia, The Free Encyclopedia, https://en.wikipedia.org/w/index.php?title=Anscombe%27s_quartet&oldid=891883779 (accessed April 17, 2019).
Justin Matejka and George Fitzmaurice, "Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing," ACM SIGCHI Conference on Human Factors in Computing Systems, https://www.autodeskresearch.com/publications/samestats (accessed December 10, 2019).