What is a p-value anyway? 34 Stories to Help You Actually Understand Statistics

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What is a p-value Anyway? offers a fun introduction to the fundamental principles of statistics, presenting the essential concepts in thirty-four brief, enjoyable stories. Drawing on his experience as a medical researcher, Vickers blends insightful explanations and humor, with minimal math, to help readers understand and interpret the statistics they read every day.

Describing data; Data distributions; Variation of study results: confidence intervals; Hypothesis testing; Regression and decision making; Some common statistical errors, and what they teach us

For all readers interested in statistics.

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Product Details

  • ISBN-13: 9780321629302
  • Publisher: Pearson
  • Publication date: 12/7/2009
  • Edition description: New Edition
  • Pages: 240
  • Sales rank: 289,511
  • Product dimensions: 6.80 (w) x 9.00 (h) x 0.50 (d)

Meet the Author

Andrew Vickers, PhD, is an Associate Attending Research Methodologist in the Department of Epidemiology and Biostatistics at Memorial Sloan-Kettering Cancer Center in New York. He is active in a variety of fields of cancer research, and also conducts original research in statistical methods, particularly with respect to the evaluation of prediction models. Dr. Vickers has been course leader for the biostatistics course at the Memorial Sloan-Kettering Cancer Center since 2001, and has taught biostatistics to medical students at Cornell Medical School since 2000. Dr. Vickers received a First Class BA in History and Philosophy of Science from the University of Cambridge and a PhD in Clinical Medicine from the University of Oxford.
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Table of Contents


0. How to read this book

1. I tell a friend that my job is more fun than you'd think: What is statistics?

Describing data

2. So Bill Gates walks into a diner: on means and medians

3. Bill Gates goes back to the diner: standard deviation and interquartile range

4. A skewed shot, a biased referee

5. You can't have 2.6 children: on different types of data

6. Why your high school math teacher was right: how to draw a graph

Data distributions

7. Chutes-and-ladders and serum hemoglobin levels: thoughts on the normal distribution

8. If the normal distribution is so normal, how come my data never are?

9. But I like that sweater: what amount of fit is a "good enough" fit?

Variation of study results: confidence intervals

10. Long hair: a standard error of the older male

11. How to avoid a rainy wedding: variation and confidence

12. Statistical ties, and why you shouldn't wear one: more on confidence intervals

Hypothesis testing

13. Choosing a route to cycle home: what p-values do for us

14. A statistical theory of how to get a five-year old boy to brush his teeth: defining the p-value

15. Michael Jordan won't accept the null hypothesis: how to interpret high p-values

16. The difference between sports and business: thoughts on the t test and the Wilcoxon test

17. Meeting up with friends: on sample size, precision and statistical power

Regression and decision making

18. When to visit Chicago: about linear and logistic regression

19. My assistant turns up for work with shorter hair: about regression and confounding

20. I ignore my child's cough, my wife panics: about specificity and sensitivity

21. Avoid the sales: statistics to help make decisions

Some common statistical errors, and what they teach us

22. One better than Tommy John: four statistical errors that are totally trivial, but which matter a great deal

23. Weed control for p-values: a single scientific question should be addressed by a single statistical test

24. How to shoot a TV episode: avoiding statistical analyses that don't provide meaningful numbers

25. Sam, 93 years old, 700 pound Florida super-granddad: two common errors in regression

26. Regression to the Mike: a statistical explanation of why an eligible friend of mine is still single

27. OJ Simpson, Sally Clark, George and me: about conditional probability

28. Boy meets girls, girl rejects boy, boy starts multiple testing

29. Some things that have never happened to me: why you shouldn't compare p-values

30. How to win the marathon: avoiding errors when measuring things that happen over time

31. The difference between bad statistics and a bacon sandwich: are there "rules" in statistics?

32. Look at your garbage bin: it may be the only thing you need to know about statistics

33. Numbers that mean something: linking math and science

34. Statistics is about people, even if you can't see the tears

Discussion Section Answers

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