The most recommended statistics books

There is so much information in this book, which gets thicker every year. I’m not going to pretend I understand it all, but I’m learning a little more every time.

The player writeups are lighthearted but insightful. Sometimes it’s almost scary looking back and seeing some of the things they projected that were so spot on. My only complaint is that everyone else in my Strat-o-matic league reads it now. It used to be an advantage.

Now I have to read it just to keep up.

A book on statistics that is interesting? Yes, actually. And The Tiger that Isn’t is more than just interesting, it’s useful. Maths was never my strong point at school, but even someone who never got the hang of quadratic equations can learn to ask useful questions when faced with bamboozlingly large numbers and dodgy ‘averages’. 

This book offers a way to see through statistics that are used to conceal information as much as to reveal it. It’s worth reading just for the section on rice and random distribution. And the tiger in the title? It’s what happens when you think you see a pattern (in this case, stripes in the undergrowth), but there is no pattern at all. 

I learned Tukey’s name about as soon as I learned that data visualization existed as more than a menu in Excel and a personal obsession. Tukey coined the term “exploratory data analysis,” and so tapped into a passion for swimming around in all the interesting rows and columns. Tukey was working before computers were widespread, and so I got a view of how he saw data: working against the constraints of pencil and paper; keeping your hand moving as fast as possible. While the explorations we can do with gigabytes of memory and powerful rendering are very different, the goal of getting information into your head as fast as possible is unchanged.

I love this book because it reminds us of the many ways that technology can affect justice.

It is tempting to think sophisticated tactics such as “predictive policing” can solve all problems relating to human bias. However, Brayne shows that data and algorithms do not eliminate bias and discretion. Instead, high-tech police tools simply make bias less overt and visible, which erodes the public’s ability to hold the police accountable.

I especially enjoyed how the book flips the script, considering diverse ways to use these tools to help the public. For example, how can municipalities use technology to analyze the underlying factors that contribute to policing problems in the first place?

I found a copy of this book in the sixties. That copy, much loved, was lost in moves and mayhem. Now, I only have a Dover reprint (water-logged during yet another move), but I have never been without and would search high and low if I were.

This is also a history of probability but with a very different focus. Ms. David was a statistician able to explain the calculations intuitively (good to assign to my students). But she was also thoroughly interested in the personalities involved. What was Galileo like? What happened to Pascal at Port-Royal?

I felt personally drawn into the historical narrative that often reads like a novel. 

This book helped me understand why advances in artificial intelligence are going to have a big impact on productivity and economic growth. I loved the analogies to old technologies like electrification of factories, and newer examples like how Team New Zealand used simulations to change racing tactics and boat design.

The book has an important, big idea at its heart. That idea is that AI helps organizations make better predications, and those better predictions allow organizations to be fundamentally redesigned to take advantage of this. This is where the AI productivity revolution comes from.

Jun Otsuka, a philosopher who also has training in statistics, zooms in on their philosophical foundations.

His book discusses the metaphysical, epistemological, and semantic assumptions on which Classical statistics, Bayesian statistics, predictive/classification AI models, and causal inference are based.

For those interested in these disciplines but who're also sensitive to the philosophical issues they raise, Otsuka's book is simply amazing. Run out and get a copy as soon as possible.   

I’m an avid fan of `More or Less,’ so this book was at the top of my list of must-reads, and I was not disappointed.

Common sense thinking seeps in by way of fascinating stories and interesting titbits. Fast-paced, light-hearted, and thought-provoking, Tim Harford does not disappoint with this book. 

From well-known authorities in the R-sphere (including a former R Core Team member), this is a long-standing text whose first edition was one of the early books intended to teach R to beginners. It provides concise instructions and examples on how R is used as a programming language before focusing on 'number-crunching' statistical methods that are typically seen as computationally intensive. One of the notable features of this book is the statistical methods at hand are not just illustrated using 'black-box' code--the reader is provided with the necessary mathematical detail to understand what's going on behind the scenes for those that are so inclined.

Mothers Matter Too is an extraordinary compendium of stories, thoughts, and statistics about women at home with children. Her helpful chart of the “rules” applicable to each stage and “role” of a woman’s life (from girlhood to cronehood) is still revelatory even years after the resurgence of the Women’s Movement. This book is also about the active listening skills critical to supporting a new mother as she struggles to regain her confidence.