How to Deceive with Statistics

Graphical comparisons of unemployment edition

Here we have a much cited graphic that made its way to the floor of the House of Representatives (discussed, among others, by Matt Yglesias):


The chart compares the number of unemployed (on the vertical axis) during a recession to the length of time into the recession (months on the horizontal axis). The three lines are the recession beginning in 1990 (blue), the recession beginning in 2001 (red), and the current recession. This makes it look like our current recession is dangerous and scary to an unprecedented degree.

However, the comparison made here is deceptive for a couple reasons. (1) We have more people in the labor force now than previously, so raw unemployment numbers are not really comparable; and (2) the US has faced many more recessions previously than these two relatively mild recessions, so omitting the others exaggerates how unusual this recession is.

A more accurate and meaningful comparison is provided at The Curious Capitalist:



The current recession is the light blue line that terminates after 12 months. As we can see, this recession is still bad, but hardly unprecedented. Based on this graphic, we might expect the current recession to be a lot like the recession beginning in July 1981, which actually ended faster than either of the recessions cited on the House floor.

If we decide to go back further to compare against all post-WWII recessions, we'll notice that the current recession is more mid-range in terms of severity and probable duration. The corresponding graphic is produced by William Polley, which I don't reproduce here since it's pretty hard to read (especially for us color-blind folk).

The point of all this is not to say how bad or not-so-bad the current recession is. The point, rather, is to emphasize a couple of key details in interpreting statistics.

Point #1: What you're measuring on your axes matters. This is why I always tell my students (even in theory classes) to LABEL.

Point #2: Beware picking and choosing data points. I'll do on data mining and pretending samples are random at some point, but for right now, just now that you should always ask why the presenter of statistics is showing you the details they are.