[prova_modena]

I recently decided to refresh my statistics knowledge after about 10 years of not touching it. My last experience with stats was during my (non-math/stats) undergraduate degree when I took some statistics coursework and ended up working fairly closely with a statistics professor during my thesis. Please keep in mind that the following recommendation comes from this amateur level of knowledge.

I read a HN comment that mentioned "Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions" by Jim Frost and picked up a copy. This book is part two of a three book series by Frost, first of which is an introductory statistics book and the last of which concerns linear regression.

I'm about 3/4 through the Hypothesis Testing book and am so impressed I just ordered the other two in the series. This book is the companion I wished I had during my undergraduate work. Reading it, I realized how much I partially digested or glossed over during my stats course. It also is a good way to reassess those statistical practices that I was taught during non-stats courses as "just how we do things." Frost is good at concisely explaining the appropriate uses of different kinds of statistical tests and their pitfalls.

What it lacks is implementation details- the book contains very few equations and no code (although I believe there is downloadable example data available). For my purposes, this works great as I most needed an overview of the different hypothesis tests and their suitability for different situations. I now have more confidence about choosing and understanding a test at a high level and can dive into specific implementation details in other sources as needed.

To be perfectly clear, I have no designs on becoming a professional statistician and I'm re-learning stats just to support a couple side projects and generally broaden my capabilities. Someone whose primary interest is the math and implementation of statistical calculations will be better served by another book.