I agree with you, although I think the problem is a focus on procedures rather than principles in general. It took me a long time to realize that a principled

I agree with you, although I think the problem is a focus on procedures rather than principles in general. It took me a long time to realize that a principled reason for gaussian parametric distributions is the maximum entropy principle. Prior to that point, it had been presented as essentially arbitrary, even by established professors. There seems to be an assumption that theoretical statistics is "too hard", and as a result there's a middle ground that gets left out. I haven't taught general stats courses in awhile (although I've taught advanced ones), but if I did, might start with Bregman divergences, and work down in the manner of your blog post. I think there's an in-between that gets lost. You can teach principles without deriving long proofs of everything along the way. Students don't get taught the underling principles and philosophies to choose from, and I think this leads to the "holy grail" issues you're referring to. It seems to be changing a bit with new interest in Bayesian methods, but that's just the tip of the iceberg.