I’ve spent almost this whole week reading (and now my head hurts. However, since correlation is not causation, I don’t feel like concluding a causal relationship between the two).
My latest read was the classic “All of statistics” by Wasserman which is a great statistics book. Its objective is to give a compact introduction to all / most of statistics in a 250 pages book, and it does so spectacularly well. If you want to understand stats, and have a good understanding of proability and analysis, I honestly can’t recommend enough “all of statistics”. This is a particularly good book for computer science / machine learning students, since they might have picked up some stats concept “on the job” but never had a formal presentation of the full framework, and they likely have all of the background knowledge.
Of course, in order to make the book compact, some things had to be cut: proofs and examples. The book offers proofs of only the most important theorems, the rest being left as an exercise to the reader. There are a few examples, but probably way less than in most books. However, and this might be a very personal opinion, I didn’t mind at all: proofs are important, but they also distract from the flow of learning about a new field such as stat: one can always come back to a more detailled reference book if one really needs to know all the details.
In a nutshell: “All of statistics” is a great book which I warmly recommend. The only drawback is that Prof. Wasserman doesn’t give the most honest presentation of bayesian inference (which, everybody knows, is the best statistical paradigm!).