A long time ago, I took a "statistics for engineers" class in order to graduate. I slept through most of the classes. It sucked, and 70 percent of it was just "distribution of the week." It did not help that homework was optional for half of it.

At some point in my professional career I started reading non-fiction books and even bought a used statistics textbook for 10 bucks on abebooks. I didn't end up actually reading it until 12 years later during the COVID lockdown. I ended up shooting for 10 pages a day, 7 days a week. If those 10 pages included review exercises, it would be a long night.

Could just be the right book at the right time, but this one really helped me understand stuff beyond the normal HS math stuff, like RMS-error, calculating correlation, the difference between standard error and standard deviation, the relationship between sample size and standard error, t-tests, and chi-squared. Working as an SRE/release engineer, this stuff really helped me overcome a lot of _bad_ canary data analysis my predecessors had constructed.

That book was the 3rd edition of Statistics by Freedman et al.[1] One thing I want to complement was getting the pedagogy right. Most chapters have strong narrative hooks, several "check your knowledge" problems, review exercises, and post chapter bullet points to assist with spaced repetition. There's even a series of "special" review exercises covering entire sections of the book, ie exams.

For the HN crowd I should also probably note that the book is almost entirely non-bayesian and not intended to prepare readers for further coursework. You will not learn normal phraseology like "IID," "random variable" or "kernel".

[1]: https://www.amazon.com/dp/B00SLB5Q72?lv=shuf&channelId=520&p...