Modern Farmer made a bar graph, and I approve. (updated to add the bottled water grahpic)
AVClub has charts/pics on alternate timelines of XMen.
Immigration is often tied in the popular imagination to poverty......Data, however, show this notion to be a caricature. See here from Scientific American.
First We Feast covers why bottled water is a ripoff.
When keeping it real (with data/statistics) goes wrong.
Unfortunately, the study and the subsequent reporting derived from the Pornhub data serves as a vivid example of six ways to make mistakes with statistics:
- Sloppy proxies
- Correlation does not equal causation
- Ecological inference
- Data naivete
- Iknow I promised I wouldn't be a scold. But this is important. You might argue why should I care so much about a bit of viral silliness from Buzzfeed? First, I would argue it's never just "all in fun" when you're declaring half of the electorate more perverted than the other half. But more importantly, I don't think the errors illustrated here are an aberration. Here's another example of blindly trusting data to reach wrong conclusions. And another. By the hand-waving measures of traditional journalism, that's three, making this a bonafide trend! I fear it will only get worse as publishing cycles become faster and the data analysis is done by single reporters harried by deadline pressure and nobody to cross-check their work before publication. I don't think we can slow this trend down, but what can data journalists do to avoid slamming into these sorts of problems at full speed? Points - Distrust the data, sniff out the problems, learn statistics, look back to go forward