Data, Graphs and Infographics

In the age of the Internet, it’s the easiest thing in the world to make very jazzy graphics of data. The term for it is “infographics”. Attractive while it may be, Tim Harford points out their danger in How to Make the World Add Up:

“Many of us who are dazzled by infographic don’t suspect a thing (about the data or the conclusions being drawn).”

Often, the aim of the infographic is even worse:

“The goal of the graphics is not to convey information but to stir feelings.”

 

Of course, not all infographics are wrong or misleading as I’d written a while back about Florence Nightingale and her charts. The problem is knowing which ones are valid and which ones are “persuasive art pretending to be a piece of statistical analysis”.

 

One tends to think that the purpose of charts is always about “inviting people to draw certain conclusions”. While that is indeed the most common use, charts can also be “exploratory” at times. Huh?

“If you’re handling a complex dataset, you’ll learn a lot by turning it into a few different graphs to see what they show. Trends and patterns will often leap out immediately if plotted the right way.”

Another use of plotting the data visually is obvious once you hear of it. Harford gives an example of height data. Say, in thousands of entries, it has some entries of heights of 50 feet! And another set with zero. The reasons must have been typos and/or data not being available:

“These problems won’t be apparent if you ask your computer to calculate an average or standard deviation… (But) if you look at a picture of the data, you’ll see the problem in a second.”

 

Like all tools, charts in data analysis can be a double-edged knife. Ultimately it comes down to the reader being alert and willing to ask questions.