内容简介

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.

作品目录

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