Additional Info: I designed a Python workflow to perform OCR on every xkcd comic, feed that text into a large language model, and ask the model whether this comic was about the category named in the title.
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xkcd comics published about artificial intelligence correlates with...
Variable | Correlation | Years | Has img? |
Air quality in Elmira, New York | r=0.93 | 6yrs | Yes! |
Air quality in Kingston, New York | r=0.87 | 6yrs | No |
The average number of likes on SciShow Space YouTube videos | r=0.78 | 9yrs | No |
How professional-sounding Technology Connections YouTube video titles are | r=0.74 | 8yrs | No |
Customer satisfaction with Rite Aid | r=0.73 | 14yrs | Yes! |
Dried manure used for fertilizer in the US | r=0.73 | 9yrs | Yes! |
Jet fuel used in Malta | r=0.72 | 15yrs | No |
Google searches for 'please clap' | r=0.65 | 16yrs | Yes! |
The number of movies Gal Gadot appeared in | r=0.65 | 14yrs | No |
Average viewer count per season of "How I Met Your Mother" | r=0.63 | 8yrs | No |
US Shoe Store Sales | r=0.61 | 15yrs | Yes! |
The distance between Venus and Earth | r=0.48 | 16yrs | No |
xkcd comics published about artificial intelligence also correlates with...
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You caught me! While it would be intuitive to sort only by "correlation," I have a big, weird database. If I sort only by correlation, often all the top results are from some one or two very large datasets (like the weather or labor statistics), and it overwhelms the page.
I can't show you *all* the correlations, because my database would get too large and this page would take a very long time to load. Instead I opt to show you a subset, and I sort them by a magic system score. It starts with the correlation, but penalizes variables that repeat from the same dataset. (It also gives a bonus to variables I happen to find interesting.)