[nwlotz]

One of the best things I was forced to do in high school was read "How to Lie with Statistics" by Darrell Huff. The book's a bit dated and oversimplified in parts, but it gave me a healthy skepticism that served me well in college and beyond.

I think the issues described in this piece, and by other comments, are going to get much worse with the (dis)information overload AI can provide. "Hey AI, plot thing I don't like A with bad outcome B, and scale the axes so they look heavily correlated". Then it's picked up on social media, a clout-chasing public official sees it, and now it's used to make policy.

[hammock]

It helps to internalize the concept that all statistics (visualizations, but also literally any statistic with an element of organization) is narrative. “The medium is the message” type of way.

Sometimes you are choosing the narrative consciously (I created this chart to tell a story), and sometimes you are choosing it unconsciously (I just want to scatter plot and see what it shows - but you chose the x and y to plot, and you chose the scatter plot vs some other framework), and sometimes it is chosen for you (chart defaults for example, or north is up on a map).

And it’s not just charts. Statistics on the whole exist to organize raw data. The very act of introducing organization means you have a scheme, framework, lens which with to do so. You have to accept that and become conscious of that.

You cannot do anything as simple as report an average without choosing which data to include and which type of average to use. Or a histogram without choosing the bin sizes, and again, the data to include.

This is all to say nothing of the way the data was produced in the first place. (Separate topic)