Love this article. It makes statistics enjoyable and accessible. Most of Olah's old stuff is also really good, especially the one on manifolds and neural networks [1]
You might like to check distill [1], it's a journal with a limited amount of content for now, but of very high quality, and C. Olah is one of the editor.
Nice post: author has good communication / teaching style, and firm grasp of the material. The visuals help make some of the concepts more intuitive. Bookmarked.
On an unrelated tangent, the way the page formats when printing is one of the best I've seen. No weird navigation cruft, and it seems there's even a style defined that sets book-style margins, such that the left and right margins alternate in length.
Thanks! This particular article went through pretty extensive feedback with colleagues where I'd print the article out, share it, and get hand written feedback. This necessitated investing in print formatting. :)
Love how the probability distributions are presented. I wish those diagrams were in the material when I was first learning probability. Would have communicated the concepts so much faster and easier.
Nice article. For those who are more interested in mosaic plots, statisticians have already done a lot of work on this issue. For R there are many nice solutions, e.g. the strucplot framework which allows to visualize complicated relationships between multiple qualitative variables (https://www.jstatsoft.org/article/view/v017i03).
One minor nitpick: the event that it rains next week is probably rather correlated with the event that it rains this week (in particular it's correlated with the season), so I don't think this is a great example of independent variables. Maybe you can separate by distance: the event that you wear t-shirt vs the event that it rains in city Y vs the event that it rains in city Z.
The visual presentation in this article is very similar with how small children are taught the theorems of multiplication and distributivity and simple series sums like triangle numbers.
[1] https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/