Telling people that they are doing statistics wrong is a cottage industry that I usually want nothing to do with, for various reasons including the fact that the naysayers are often blindly repeating stuff they heard but do not understand. But, Alex Reinhart, in Statistics Done Wrong: The Woefully Complete Guide, does not do that, and this is a book that is worth reading for anyone who either generates or needs to interpret statistics.
Most of the 10 chapters that address specific technical problems with statistics, where they are misused or misinterpreted, are very helpful in guiding a reader in how to think about statistics, and certain fallacies or common errors may well apply to a particular person's work on a regular basis. I've put the table of contents below so you can see how this may apply to you. This is a worthy addition to the bookshelf. Get this book and stop doing your stats wrong!
The author is a grad student and physical scientist at Carnegie Mellon.
Here's the table of contents:
Chapter 1: An Introduction to Statistical Significance
Chapter 2: Statistical Power and Underpowered Statistics
Chapter 3: Pseudoreplication: Choose Your Data Wisely
Chapter 4: The p Value and the Base Rate Fallacy
Chapter 5: Bad Judges of Significance
Chapter 6: Double-Dipping in the Data
Chapter 7: Continuity Errors
Chapter 8: Model Abuse
Chapter 9: Researcher Freedom:Good Vibrations?
Chapter 10: Everybody Makes Mistakes
Chapter 11: Hiding the Data
Chapter 12: What Can Be Done?
Interesting. Usually you have to read the Bell Curve, anything by Regnerus, Lott, or climate deniers to see the wrong way to do statistic
It's a good one. I also enjoy the classic "How to Lie With Statistics" by Darryl Huff from back in the 1950s. Still available in crushed stained tree material form, but also on the newfangled electric gadget formats.