The Best Example of Bad Graphs

Those of you in the UK (or elsewhere) who read the Telegraph may have noticed an article recently called “How the Fed triggered the Arab Spring uprisings in two easy graphs”. If you want the gist of it, here is the first graph:

Now, as most economists learn, correlation does not equal causation. But let’s assume for the author’s sake that he has a point. What should we think when we see commodity prices rising (starting June 2010) and the Fed’s Treasury purchases rising after the fact (around Aug-Sept 2010)? Yes, if we want to read causation, then we’d say that the rise in food prices caused the Fed Treasury purchases, and not vice versa (as the article claims).

The second graph, which we show below, displays the correlation between, well, we’re not sure. The explanation would lead us to believe they’re equating price of wheat with revolutions (which we certainly won’t deny here), but look closely: the x-axis has the Peak wheat prices in 1845-8 vs. the Average wheat prices from 1820 to 1845 AND the 2008 wheat prices vs. the Average wheat prices from 1991-2007. The Y-axis, on the other hand, has the Peak wheat prices in 1845-8 vs. the Average wheat prices from 1838 to 1845 AND the 2008 wheat prices vs. the Average wheat prices from 2000-2007 (why different start dates for the averages on the axes?).

Again, there seems to be some correlation here. As best we can tell, if you compare the exact same data but follow slightly different average start dates (but not end dates) then the numbers will be correlated. It would be very surprising indeed if there were no correlation here.

In other words, this is some of the shoddiest use of graphs you could ever find in a well known publication. If you are looking for examples of bad uses of statistics, we recommend bookmarking this article.

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