Nonetheless, the book is very well written and all figures and examples show great attention to detail. I found Gelman et al Regression and Other Stories better for teaching newcomers, and surprisingly insightful. Statistical Rethinking is a good choice for a second course, but perhaps too informal at that stage.
- the book tries to be practical and applicable for science
- the book assumes some amount of mathematical maturity and ability to fiddle with somewhat simple data
- the book is not about mathematical statistics – no proving things about maximum likelihood estimators
- the book doesn’t teach you about programming in R
What if you know math but not stats? How much stats do I need to know before you think this isn’t good to browse?
Wish I knew… I guess I’ll have to find out the hard way.
However, it is a bit too cautious about scaring readers away from the details of how things work. Honestly, I disagree with the parent that it's a bad book for the more mathematically inclined, since I can't think of any other book that gets you solving practical problems faster. But, if you have a strong math (or computational) background, you will be craving a deeper look under the hood.
ET Jaynes' Probability Theory: the Logic of Science is, imho, the best book for someone who wants to really understand the theory and reasoning behind statistics and is comfortable lots of mathematical thinking.
For a more practical (than Jaynes) but still more detailed book on statistics then I would recommend Bayesian Modeling and Computation in Python. Not quite as easy reading as Statistical Rethinking but there will be no mystery as to what's happening.
Statistical Rethinking (2022 Edition) - https://news.ycombinator.com/item?id=29956390 - Jan 2022 (124 comments)
Statistical Rethinking [video] - https://news.ycombinator.com/item?id=29780550 - Jan 2022 (10 comments)
Statistical Rethinking: A Bayesian Course Using R and Stan - https://news.ycombinator.com/item?id=20102950 - June 2019 (14 comments)
I’m mostly a Python guy, and didn’t find it particularly hard to get this going. Although I’m always left scratching my head when using RStudio/Renv/R. It’s such a horrible environment (always hanging, crashing, slow, the tooling sucks ass). I refuse to believe that I’m the only person who has RStudio hang and require a restart or get stuck on some uninterruptible process and requires forcing killing it at least once a day.
Yes, I think I've been trained by crashes to subconsciously limit interactions with the RStudio GUI while something is running, e.g resizing a window seems to be surefire way to cause a crash.
Combined with OrbStack (for Docker on MacOS) and Quarto (which is a nice Markdown-based alternative to Jupyter) I would go so far as to call the experience pleasant.
I don't remember running into version-related problems. Maybe I didn't make it as far in the book as you.
Ultimately there is no good solution- really in any language- that I know of for using old unmaintained packages on a modern version of the language.
I think it’s a magnificent book - definitely repays the time to work though in detail.
I thought the book was only so-so, but required to support the nuances of what he discussed in class.