- Paperback: 240 pages
- Publisher: Pearson; 1 edition (November 28, 2009)
- Language: English
- ISBN-10: 0321629302
- ISBN-13: 978-0321629302
- Product Dimensions: 6.9 x 0.5 x 9.2 inches
- Shipping Weight: 10.4 ounces (View shipping rates and policies)
- Average Customer Review: 52 customer reviews
- Amazon Best Sellers Rank: #69,522 in Books (See Top 100 in Books)
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What is a p-value anyway? 34 Stories to Help You Actually Understand Statistics 1st Edition
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From the Back Cover
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.
About 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.
Top customer reviews
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In this introduction, the author summarizes the content well by noting that "the first 12 chapters deal with some basics, such as averages, variation, distributions and confidence intervals. I then have a few chapters on hypothesis testing and p-values, before discussing regression - the statistical method I use most in my work - and decision making - which generally should be, but often isn't, what statistics is about. The last third of the book, starting from the chapter 'One better than Tommy John', is devoted to discussing a wide variety of statistical errors."
"If it seems odd to devote so much of a book to slip-ups, it is because I have a little theory that 'science' is just a special name for 'learning from our mistakes'. When I teach, I give bonus points for any student giving a particularly dumb answer because those are the ones we really learn from. In fact, I don't think you can really understand, say, a p-value, without seeing some of the ways it has been misused and thinking through why these constitute mistakes. So please don't blow these chapters off thinking you've read the stuff you'll be examined on: the final chapters will really fill in your statistical knowledge."
Chapters that I especially appreciated include Chapter 9 on degree of normal distribution fit, Chapter 11 on variation and confidence intervals, Chapters 13, 14, 15, 23, and 29 on p-values, Chapter 17 on sample size, precision, and statistical power, Chapter 19 on regression and confounding, Chapter 20 on specificity and sensitivity, Chapter 21 on decision analysis, Chapter 22 on statistical errors, and Chapter 32 on science, statistics, and reproducibility. The discussion section that comprises the last 25% of what the author shares here works through questions posed at the end of each of the 34 chapters, and much of the value that I personally obtained from this text can be found in this section.
In my opinion, it is the rare reader who will not find any author discussions worth remembering, because Vickers simply tells it like it is. In Chapter 5, for example, the author describes a continuous variable as one that can take "a lot of different values", and in the discussion section for this chapter he points out that "statisticians disagree on this point (statisticians disagree on a lot of points, which just goes to show how much of statistics is a judgement call)." In Chapter 13, the author indicates that he "provided strong evidence" for something, and in the discussion section comments that "'proof' is not a word often used by scientists."
"Statisticians are particularly careful with the word 'proof', because they are keenly aware of the limitations of data, and the important role that chance plays in any set of results. Statisticians normally use the word 'proof' only to refer to mathematical relationships between formulas. The point here is that you don't use data to do math theory, so you aren't subject to the limitations of data, and so you can go about really claiming to have 'proved' something. It is certainly unwise to think that you can prove anything by applying a statistical test to a data set."
In Chapter 9, the author notes that "statisticians don't typically seem to worry too much about whether or not the data are a close fit to the normal distribution because they realize that statistics isn't football, and no one is going to throw a flag and send you back 10 yards if you are caught breaking the rules. In fact, there aren't really many 'rules' at all." After quoting one sentence from a scientific paper describing a clinical trial in Chapter 22, the author works through the sentence and discusses each of the four statistical errors made by the researcher, and discusses why he cares about such errors.
"Many people seem to think that we statisticians spend most of our time doing calculations, but that is perhaps the least interesting thing we do. Far more important is that we spend time looking at numbers and thinking through what they mean. If I see any number in a scientific report that is meaningless - a p-value for baseline differences in a randomized trial, say, or a sixth significant figure - I know that the authors are not being careful about what they are doing, they are just pulling numbers from a computer printout. Statistics is more than just cutting and pasting from one software package to another. We have to think about what the numbers mean and the implications for our scientific question." Well said.
While I really like it, I do not love this book because it is missing guidance for people who want to learn more. This will desensitize people who are afraid of statistics but I fear that they will then flounder there way to less friendly books and relearn why they hate math. It would have been better if Vickers pointed the readers to other friendly statistics books for people with different backgrounds, like Biostatistics: The Bare Essentials, 3e or Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking or Biostatistics For Dummies (For Dummies (Health & Fitness)) for people who like biology instead of math. Even with this serious shortcoming, this is an excellent (but rather pricy) book.
In my own view, this book is heaven sent i would recommend it to everyone who cares to learn and understand not just the how, but the why we conduct stats. I reside in Nigeria and had to wait for 3 weeks before receiving the book cos I had it delivered to a friend's residence in the US. He was visiting Nigeria for a couple of weeks.
All in all, it is worth the purchase and the wait
Andrew J. Vickers, thanks for this book.
So what does the author do?
He takes all of the major introductory topics in medical statistics (e.g. descriptive statistics, hypothesis testing, parametric versus non-parametric testing etc.) and conveys in a nice conversational manner. One can imagine he is your stat expert friend who is explaining patiently to you over coffee and some stories these ideas. For the more serious students you'll still need your heavy weight text with Z-scores, degrees of freedom tables and so on but for people who just need to understand medical statistics this is a terrific book.
This is ideal for students, residents and attendings who need to better understand statistics but not to calculate them.