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 physics correlates with...
| Variable | Correlation | Years | Has img? |
| The number of movies Florence Pugh appeared in | r=0.88 | 9yrs | No |
| Air pollution in Dodge City | r=0.86 | 17yrs | No |
| The number of first-line supervisors of police and detectives in Idaho | r=0.81 | 13yrs | No |
| The number of computer network support specialists in Nevada | r=0.79 | 11yrs | No |
| Popularity of the 'is this a butterfly' meme | r=0.79 | 17yrs | No |
| The number of fire inspectors in Iowa | r=0.75 | 13yrs | Yes! |
| Google searches for 'best place to work' | r=0.74 | 17yrs | No |
| Google searches for 'how to apply a tourniquet' | r=0.73 | 17yrs | Yes! |
| The number of chiropractors in Mississippi | r=0.71 | 16yrs | No |
| Google searches for 'why do i have a migraine' | r=0.6 | 17yrs | No |
xkcd comics published about physics 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.)