Four Books Worth Owning

Below are listed four books on statistics which I feel are worth owning. They largely take a "traditional" statistics perspective, as opposed to a machine learning/data mining one. With the exception of "The Statistical Sleuth", these are less like textbooks than guide-books, with information reflecting the experience and practical advice of their respective authors. Comparatively few of their pages are devoted to predictive modeling- rather, they cover a host of topics relevant to the statistical analyst: sample size determination, hypothesis testing, assumptions, sampling technique,  etc.

• "Common Errors in Statistics", by Good and Hardin
• "Statistical Rules of Thumb", by van Belle
• "Your Statistical Consultant", by Newton and Rudestam
• "The Statistical Sleuth", by Ramsey and Schafer

I have not given ISBNs since they vary by edition. Older editions of any of these will be valuable, so consider saving money by skipping the latest edition.

A final clarification: I am not giving a blanket endorsement to any of these books. I completely disagree with a few of their ideas. I see the value of such books in their use as "paper advisers", with different backgrounds and perspectives than my own.