Statistics Done Wrong

Alex Reinhart is a statistics instructor and PhD student at Carnegie Mellon University. He received his BS in physics at the University of Texas at Austin and does research on locating radioactive devices using statistics and physics.

Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong.

Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics.

You'll find advice on:
Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan
How to think about p values, significance, insignificance, confidence intervals, and regression
Choosing the right sample size and avoiding false positives
Reporting your analysis and publishing your data and source code
Procedures to follow, precautions to take, and analytical software that can help

Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know.

The first step toward statistics done right is Statistics Done Wrong.

A guide to the most common statistical errors and how to avoid them, for anyone working with statistics. Helps readers make sense of a constant barrage of data and statistics History of strong sales of similar titles like How to Lie with Statistics Targets a growing number of professionals working with statistics

Introduction
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?
Notes
Index