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.
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Alex Reinhart is a statistics instructor and PhD student at Carnegie Mellon University. He received his BS in physics at the University of Texas at Austin and does research on locating radioactive devices using statistics and physics.
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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
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译者注:p值就是当原假设为真时,比所得到的样本观察结果更极端的结果出现的概率。
通常来说,我们观测的是由于巧合或随机变化导致的差异,所以当观测差异大于随机产生的差异时,统计学家称之为“统计意义上的显著区别”
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