Editor’s Note: Today I am pleased to introduce you to a new regular contributor to Research Access, Dr. Dana Griffin. Dr. Griffin will be starting off by doing a series of lessons on data visualization, beginning with today’s entry.
It is incredibly easy to draw erroneous conclusions from data that is presented visually. Nowhere has this lesson been more starkly burned into my mind than in the middle of the fabled Hood to Coast Relay – an annual 199-mile running race/ mobile party/ physical suffer-fest played out across the roads of Western Oregon.
At one of the baton exchange areas, I struck up a conversation with a fellow runner who was also waiting for a teammate to come down the finisher’s chute. “I was not expecting that last run to be so tough” he said, nursing what looked like wickedly painful injuries to both knees. “I volunteered to do that segment because I thought it was about the same as my friend’s.”
We opened the official guidebook to compare the two course maps.
“Oh!” I exclaimed. (Cue geek mode). “I know what it is… the maps have different values on the y-axis. The shapes of the lines are similar, but the increments of y are different. No wonder your run was tougher – you had extra elevation to deal with!”
“Um, okay.“ Long pause. “I think I should go find my friends.”
With that, he hobbled off into the cool Oregon evening, preferring to leave his teammate to arrive unheralded rather than rap with some random girl about the perils of neglecting the y-axis.
So much for small talk.
Whether in running or research, it’s easy to be tripped up, confused, or mislead by data when it is displayed visually. I would argue that basic literacy in data and statistics includes being able to:
• Recognize visuals and graphics that display data poorly.
• Suggest ways that the data could be displayed more accurately.
• Use good visual-display-of-data practices our own work.
As part of Research Access’ “Back to School” series, we’ll revisit basic principles in visual and graphical display of data. Several of my upcoming posts include:
• How poor visual displays of data can shortchange what would otherwise be substantively interesting research findings.
• Not all bad graphics intend to mislead… some are just genuinely awful. Examples of The Good, The Bad, and the Terrible.
• How Do I Visually Illustrate the Results of my Fancy Statistical Test?
- Tips for visually representing correlations, sample/ panel characteristics, and regressions
• The Ultimate List of Do’s and Don’ts for Displaying Data Visually
And much, much more.
Data Visualization Lesson 2: Think of Grandma
Data Visualization Lesson 3: Abela’s Rubric
Data Visualization Lesson 4: The Best Pies are Desserts
Data Visualization Lesson 5: Ninth Grade Algebra Wasn’t Worthless After All
Data Visualization Lesson 6: The Ultimate List of Dos and Don’ts