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.
Author(s): Alex Reinhart
Edition: 1
Publisher: No Starch Press
Year: 2015
Language: English
Commentary: Vector PDF
Pages: 176
City: San Francisco, CA
Tags: Statistics; Best Practices; Probability Theory; Statistical Inference; Anti-Patterns
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
