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Statistics Done Wrong
Statistics Done Wrong is a guide to the most popular statistical errors and slip-ups committed by scientists every day, in the lab and in peer-reviewed journals. Many of the errors are prevalent in vast swaths of the published literature, casting doubt on the findings of thousands of papers. Statistics Done Wrong assumes no prior knowledge of statistics, so you can read it before your first statistics course or after thirty years of scientific practice.
- Author
- Alex Reinhart
- Publisher
- Carnegie Mellon University
- Source
- LibreTexts
- License
- CC BY
- Language
- EN
- Category
- Basic Sciences
Contents· 34 sections
- 01: An Introduction to Data Analysis
- 1.01: Data Analysis
- 1.02: The Power of p Values
- 02: Statistical Power and Underpowered Statistics
- 2.01: Statistical Power
- 2.02: The Power of Being Underpowered
- 2.03: The Wrong Turn on Red
- 03: Pseudoreplication- Choose Your Data Wisely
- 04: The p Value and the Base Rate Fallacy
- 4.01: Prelude to p Values
- 4.02: The Base Rate Fallacy in Medical Testing
- 4.03: Taking up Arms Against the Base Rate Fallacy
- 4.04: If at First You Don't Succeed Try Try Again
- 4.05: Red Herrings in Brain Imaging
- 4.06: Controlling the False Discovery Rate
- 05: When Differences in Significance Aren't Significant Differences
- 5.01: Differences in Significance
- 5.02: When Significant Differences are Missed
- 06: Stopping Rules and Regression to the Mean
- 6.01: Rules of the Game
- 6.02: Truth Inflation
- 6.03: Little Extremes
- 07: Researcher Freedom- Good Vibrations
- 08: Everybody Makes Mistakes
- 09: Hiding the Data
- 9.01: Handling Data
- 9.02: Just Leave out the Details
- 9.03: Science in a Filing Cabinet
- 10: What Have We Wrought
- 11: What Can be Done
- 11.01: Statistical Education
- 11.02: Scientific Publishing
- 11.03: Your Job
- 12: Conclusion