Statistics Done Wrong: The Woefully Complete Guide

Description

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. Praise for Statistics Done Wrong About the Author Brief Contents Contents in Detail Preface Acknowledgments Introduction Chapter 1: An Introduction to Statistical Significance The Power of p Values Psychic Statistics Neyman-Pearson Testing Have Confidence in Intervals Chapter 2: Statistical Power and Underpowered Statistics The Power Curve The Perils of Being Underpowered Wherefore Poor Power? Wrong Turns on Red Confidence Intervals and Empowerment Truth Inflation Little Extremes Tips Chapter 3: Pseudoreplication: Choose Your Data Wisely Pseudoreplication in Action Accounting for Pseudoreplication Batch Biology Synchronized Pseudoreplication Tips Chapter 4: The P Value and the Base Rate Fallacy The Base Rate Fallacy A Quick Quiz The Base Rate Fallacy in Medical Testing How to Lie with Smoking Statistics Taking Up Arms Against the Base Rate Fallacy If At First You Don't Succeed, Try, Try Again Red Herrings in Brain Imaging Controlling the False Discovery Rate Tips Chapter 5: Bad Judges of Significance Insignificant Differences in Significance Ogling for Significance Tips Chapter 6: Double-Dipping in the Data Circular Analysis Regression to the Mean Stopping Rules Tips Chapter 7: Continuity Errors Needless Dichotomization Statistical Brownout Confounded Confounding Tips Chapter 8: Model Abuse Fitting Data to Watermelons Correlation and Causation Simpson's Paradox Tips Chapter 9: Researcher Freedom: Good Vibrations? A Little Freedom Is a Dangerous Thing Avoiding Bias Tips Chapter 10: Everybody Makes Mistakes Irreproducible Genetics Making Reproducibility Easy Experiment, Rinse, Repeat! Tips Chapter 11: Hiding the Data Captive Data Obstacles to Sharing Data Decay Just Leave Out the Details Known Unknowns Outcome Reporting Bias Science in a Filing Cabinet Unpublished Clinical Trials Spotting Reporting Bias Forced Disclosure Tips Chapter 12: What Can Be Done? Statistical Education Scientific Publishing Your Job Notes Introduction Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Index Colophon Updates