5 Statistics Books You Should Read

Since I’m on a bit of a book list kick at the moment, I thought I’d put together my list of the top 5 stats and critical thinking books people should read if they’re looking to go a bit more in depth with any of these topics.  Here they are, in no particular order:

If you’re looking for….a good overview:
How to Lie with Statistics

This is one of those books that should be given to every high school senior, or maybe even earlier. In fact, I know more than a few people who give this out as a gift. It’s 60 years old, but it still packs a punch. It’s written by a journalist, not a statistician, so it’s definitely for the layperson.

If you’re looking for….something a bit more in depth:
Thinking Statistically

If you want to know how to think about statistical concepts without actually having to do any math, this book is for you. What I would get my philosophy major brother for Christmas so we could actually talk about things I’m interested in for once.

If you’re looking for….something a little more medical:
Bad Science: Quacks, Hacks, and Big Pharma Flacks

Ben Goldacre is a doctor from the UK, so it’s no surprise he focuses mostly on bad interpretations of medical data.  He calls out journalists, popular science writers and all sorts of other folks in the process though, and helps consumers be more educated about what’s really going on.

If you’re looking for….something that will actually help you pass a class:
The Cartoon Guide to Statistics

Not a very advanced class, but a pretty solid re-explaining of stats 101. I keep this one at work and hand it out when people have basic questions or want to brush up on things.

If you’re looking for….a handy guide for those who actually get stats:
Statistical Rules of Thumb

This is one of the few textbooks I actually bought just to have on hand and to flip through for fun. It’s pricey compared to a regular book, but worth it if you’re using statistics a lot and need a in depth reference book. It contains all those “real world” reminders that statisticians can forget if they’re not paying attention. With different sections for basics, observational studies, medicine, etc, and advice like “beware linear models” and “never use a pie chart”, this is my current favorite book.

As always,  further recommendations welcome!