Ask HN: What is your favorite book for learning statistics?

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.

Elements of Statistical learning is from the same authors (mostly) as ISLR but it's longer and goes a bit deeper on the theory. Also, Computer Age Statistical Inference which is newer and covers more material.

Other big titles - Pattern Recognition and Machine Learning (Bishop), Machine Learning: A Probabilistic Perspective (Murphy) - these last two weren't for me but different people respond to different approaches so check them out especially if the first two books don't click for you.

Not at all, take a look - https://hastie.su.domains/CASI_files/PDF/casi.pdf

When I studied economics in college, statistics was taught as a math course with virtually no straightforward explanation of the main concepts. Later as a graduate student in sociology, I helped teach statistics to undergraduates using an introductory textbook that featured an intuitive and conceptual presentation of statistics, and for the first time I really grasped the main ideas. By then it was no longer necessary to manually perform the advanced mathematical operations for statistical analysis, because the students were encouraged to use SPSS and other software tools. If you are looking for an outstanding introduction to statistics, I highly recommend the following textbook:

Healey, Joseph (2014) Statistics: A Tool for Social Research, 10th Edition

For those seeking a good conceptual knowledge of statistics based on real-world examples, including criticism of the limitations of statistical analysis, I would suggest these excellent studies:

Lewis, Michael (2010) The Big Short: Inside the Doomsday Machine

Paulos, John Allen (1988) Innumeracy: Mathematical Illiteracy and Its Consequences

Silver, Nate (2012) The Signal and the Noise: Why So Many Predictions Fail, but Some Don't

Taleb, Nassim Nicholas (2005/10) Fooled by Randomness, 2E; Black Swan: Impact of the Highly Improbable, 2E

The only right answer is All of Statistics: A Concise Course in Statistical Inference

https://www.stat.cmu.edu/~larry/all-of-statistics/