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.

Table of Contents:

;
About the Author;
Preface;
Acknowledgments;
Introduction;
Chapter 1: An Introduction to Statistical Significance;
1.1 The Power of p Values;
1.2 Have Confidence in Intervals;
Chapter 2: Statistical Power and Underpowered Statistics;
2.1 The Power Curve;
2.2 The Perils of Being Underpowered;
2.3 Confidence Intervals and Empowerment;
2.4 Truth Inflation;
Chapter 3: Pseudoreplication: Choose Your Data Wisely;
3.1 Pseudoreplication in Action;
3.2 Accounting for Pseudoreplication;
3.3 Batch Biology;
3.4 Synchronized Pseudoreplication;
Chapter 4: The P Value and the Base Rate Fallacy;
4.1 The Base Rate Fallacy;
4.2 If At First You Don?t Succeed, Try, Try Again;
4.3 Red Herrings in Brain Imaging;
4.4 Controlling the False Discovery Rate;
Chapter 5: Bad Judges of Significance;
5.1 Insignificant Differences in Significance;
5.2 Ogling for Significance;
Chapter 6: Double
-Dipping in the Data;
6.1 Circular Analysis;
6.2 Regression to the Mean;
6.3 Stopping Rules;
Chapter 7: Continuity Errors;
7.1 Needless Dichotomization;
7.2 Statistical Brownout;
7.3 Confounded Confounding;
Chapter 8: Model Abuse;
8.1 Fitting Data to Watermelons;
8.2 Correlation and Causation;
8.3 Simpson?s Paradox;
Chapter 9: Researcher Freedom: Good Vibrations?;
9.1 A Little Freedom Is a Dangerous Thing;
9.2 Avoiding Bias;
Chapter 10: Everybody Makes Mistakes;
10.1 Irreproducible Genetics;
10.2 Making Reproducibility Easy;
10.3 Experiment, Rinse, Repeat;
Chapter 11: Hiding the Data;
11.1 Captive Data;
11.2 Just Leave Out the Details;
11.3 Science in a Filing Cabinet;
Chapter 12: What Can Be Done?;
12.1 Statistical Education;
12.2 Scientific Publishing;
12.3 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;
Colophon;
Updates;