Author Alex Reinhart's s bio says "I’m a PhD student in statistics at Carnegie Mellon University, after previously earning my BSc in physics at the University of Texas at Austin while doing research on statistical methods to detect unexpected radioactive sources using mobile detectors. (I preferred to call it “finding radioactive people at football games.”) I now work on statistical models to understand and predict where crimes occur."
He obviously know his stuff - it just wasn't the stuff that I expected when I picked up the book.
As a mathematician I like to read books on various math disciplines especially topology, set theory and so on but also some applied topics such as probability and stats. I wrongly assumed that Statistics Gone Wrong would be about how methods and theorems of applied stats can lead to inaccurate results. The book is more for people who analyze data and make statistical predictions.
Don't get me wrong. The author does a very complete job in demonstrating how using stats can lead to inaccurate and even false conclusions. He starts with defining p values and gives examples of how they can lead to inaccuracies.
The use of examples is interesting such as the published results of the "Right turn on Red" data.
Statistics Done Wrong is a guide to the most popular statistical errors and slip-ups committed by scientists every day, in the lab and in peer-reviewed journals. Many of the errors are prevalent in vast swaths of the published literature, casting doubt on the findings of thousands of papers. Statistics Done Wrong assumes no prior knowledge of statistics, so you can read it before your first statistics course or after thirty years of scientific practice."
It's a good, solid book. Just don't expect it to be about probability and statistics. It is about the errors that can be made in analyzing data.