There is also Cox's theorem, that essentially states that probability theory is the only sound system that extends logic in the presence of uncertainty, to continuous values (between 0 and 1) instead of the two values 0 and 1.
I make the connection to demonstrate that Bayes is not some arcane statistical artifact, but qualitatively in line with intuition, albeit miscalibrated. This opens up avenues to then move towards ideal bayesian-ness.
And just to fill up the word count; if you haven't read up on the major statistical paradoxes it is possible you'll enjoy them. https://en.wikipedia.org/wiki/Category:Statistical_paradoxes for your attention. If you are playing with stats for the framework then the other half of the fun is delving into the paradoxes; knowing them by heart is a great trick for interpreting evidence. Simpson's, Berkson's and the Elevator Paradox explain a lot of life.
I really enjoyed this explanation of Simpson's Paradox: http://michaelnielsen.org/reinventing_explanation/
http://causality.cs.ucla.edu/blog/index.php/2020/07/06/race-...
Though we don't always work that way. Consider all the cognitive bias we're susceptible to. You have to account for that.
We update our beliefs based on new information, but the whole point of Bayesinaism via Bayes Theorem is updating it by a very specific amount based on evidence strength. Nobody is approximately Baysian in their thought process, and I doubt most people can even be trained to be. Statistics is a pen & paper exercise for the most part.
In my experience, which is not negligible, is the hardest part of statistics is talking people down from beliefs they have settled on because of something that looks like statistical evidence, but in fact is not.