I really wish that I had something comprehensive to suggest. There are some things that I think everyone who deals with statistics should read, such as Freedman's 'Statistical Models and Shoe Leather'[^1] and Tukey's 'Exploratory Data Analysis'[^2]. Pearl's 'Causal Inference in Statistics' is the best introduction I know of to issues of causality as we understand them today. For actual inference, the basis is one player game theory (aka, decision theory). I learned it from Kiefer's 'Introduction to Statistical Inference,' which sets out the theory very nicely in the first few chapters. That's a starting point at least. There are some interesting courses[^3] that try to teach statistics via resampling that seem pedagogically very valuable. Resampling does build intuition nicely and using it gets people over their squeamishness around using randomized procedures.
I really wish that I had something comprehensive to suggest. There are some things that I think everyone who deals with statistics should read, such as Freedman's 'Statistical Models and Shoe Leather'[^1] and Tukey's 'Exploratory Data Analysis'[^2]. Pearl's 'Causal Inference in Statistics' is the best introduction I know of to issues of causality as we understand them today. For actual inference, the basis is one player game theory (aka, decision theory). I learned it from Kiefer's 'Introduction to Statistical Inference,' which sets out the theory very nicely in the first few chapters. That's a starting point at least. There are some interesting courses[^3] that try to teach statistics via resampling that seem pedagogically very valuable. Resampling does build intuition nicely and using it gets people over their squeamishness around using randomized procedures.
[^1]: http://psychology.okstate.edu/faculty/jgrice/psyc5314/Freedm...
[^2]: Sadly really hard to find. We need a cheap reprint of this.
[^3]: https://resample.statistics.com/intro-text-online/