Description
Book SynopsisScientific 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 Proced
Table of ContentsIntroduction
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
