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Statistics Done Wrong: The Woefully Complete Guide, by Alex Reinhart
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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
The first step toward statistics done right is Statistics Done Wrong.
- Sales Rank: #36923 in Books
- Published on: 2015-03-16
- Original language: English
- Number of items: 1
- Dimensions: 8.10" h x .50" w x 5.90" l, .65 pounds
- Binding: Paperback
- 176 pages
Review
"If you analyze data with any regularity but aren't sure if you're doing it correctly, get this book." -- Nathan Yau, FlowingData
"Of all the books that tackle these issues, Reinhart's is the most succinct, accessible and accurate." -- Tom Siegfried, Science News
"A spotter's guide to arrant nonsense cloaked in mathematical respectability." -- Gord Doctorow, BoingBoing
From the Author
What goes wrong most often in scientific research and data science? Statistics.
Statistical analysis is tricky to get right, even for the best and brightest. You'd be surprised how many pitfalls there are, and how many published papers succumb to them. Here's a sample:
- Statistical power. Many researchers use sample sizes that are too small to detect any noteworthy effects and, failing to detect them, declare they must not exist. Even medical trials often don't have the sample size needed to detect a 50% difference in symptoms. And right turns at red lights are legal only because safety trials had inadequate sample sizes.
- Truth inflation. If your sample size is too small, the only way you'll get a statistically significant result is if you get lucky and overestimate the effect you're looking for. Ever wonder why exciting new wonder drugs never work as well as first promised? Truth inflation.
- The base rate fallacy. If you're screening for a rare event, there are many more opportunities for false positives than false negatives, and so most of your positive results will be false positives. That's important for cancer screening and medical tests, but it's also why surveys on the use of guns for self-defense produce exaggerated results.
- Stopping rules. Why not start with a smaller sample size and increase it as necessary? This is quite common but, unless you're careful, it vastly increases the chances of exaggeration and false positives. Medical trials that stop early exaggerate their results by 30% on average.
About the Author
Alex Reinhart is a statistics Ph.D student at Carnegie Mellon University who received his B.S. in physics at the University of Texas, Austin. He teaches introductory statistics at Carnegie Mellon.
Most helpful customer reviews
98 of 100 people found the following review helpful.
Appealing
By Dimitri Shvorob
Let me front-load the criticism. I wish an experienced statistics instructor had reviewed the manuscript. The book does better in its second half, where it discusses what I would call problems with empirical-research culture, than in its first half, which has more textbook statistics. The author neglects to explain the basics - things like "sample", "statistic", "sampling distribution", "conditional probability" - and often confuses matters by bringing in issue Y when setting out to discuss issue X. (Appropriately, a section named "Confounding Confounders" is itself confounded: we start talking about "coarsening" data (not what I expected based on the title, by the way; a Y-for-X switch already took place), then get into something else. I will single out the introduction to the "base-rate fallacy" as another weak spot). A choice to be non-technical means that solutions to some problems cannot be effectively presented - although sometimes they are suggested after all. The "woefully complete" part of the title is, I take it, tongue-in-cheek, so no quibbles there.
A few "similar" books come to mind, including (a) the drier "Common errors in statistics" by Phillip Good, (b) the three terrific popular books by Ben Goldacre - "Bad science", "Bad pharma" and "I think you'll find it's a bit more complicated than that" - and (c) the elegant "Understanding the new statistics" by Geoff Cumming. (I have not seen "How to lie with statistics" by Huff and Geis). Reinhart's book is more "big-picture" than Good's, and broader than Goldacre's or Cumming's. (The latter is a perfect "single-issue" book; the former are not specifically about cataloging statistics errors).
Statistical semi-literacy of empirical researchers is a serious problem, and any effort to improve the situation is to be lauded. Alex Reinhart's book - engagingly written, and nicely produced (and fairly cheaply sold) by No Starch Press - is a force for good, and one which can have a material impact.
41 of 44 people found the following review helpful.
A Primer on Doing Statistics Right
By John Jacobson
As the title suggests, this book is a diatribe against the misuse of statistics. While it is not a "complete" guide, it does discuss many the the errors that creep into studies published in even the most prestigious journals. Issues addressed include the use and misuse of "p" values, confidence intervals, an exploration of the meaning of "null hypothesis," power analysis, and the base rate fallacy.
While each of these chapters illuminates "gotchas" that creep into statistical analysis, perhaps the most important part of the book looks at the choice of study populations. There is a brief description of many the biases that go unrecognized, including confirmation bias, publication bias, observer bias, and reporting bias. There is a checklist designed to help the researcher avoid creating a study population that is biased toward a particular result.
The book closes with an appeal to improve the teaching of statistics. The lecture method of teaching is not effective, the author touts "peer instruction," an approach that is similar to a seminar rather than a ordered didactic lecture presentation. This is a worth while book for those interested in extracting the maximum value from their statistical analyses. It is written in an engaging manner, the case presentations are germane to the subject, and despite being an easy read, there is a great deal of solid material. Recommended!
27 of 28 people found the following review helpful.
Best and WORST Practices in Using Statistical Analysis. Especially by Working Scientists but Understandable by All--Revealing
By Ira Laefsky
The author was an Undergraduate Physics major at the University of Texas, and subsequently became a Statistics Ph.D. student and Instructor at CMU after being surprised by the lack of Statistics knowledge and use of best practices by working scientists. I am a Computer Engineer and MBA with one basic course in Statistics acquired during my stay at the Wharton School 30 some years ago, and seeking a greater knowledge of Statistical Principles for work in Human Computer Interaction and Data Science in my present endeavors. I like the author am distressed by the lack of guidance received by professionals and working scientists in properly applying statistical methods to deciding the conclusions to be drawn from a research investigation. Mr. Reinhart properly points out that most individuals who have been exposed to statistics at all lack a fundamental understanding of such basic concepts as the P-Value--("the probability, under the assumption that there is ...no true difference, of collecting data that shows a difference equal to" or greater than that which you actually observed). The author emphasizes the importance of Statistical Power, the probability that a study "will distinguish an effect of a certain size from pure luck".
Many Statistical and Logical Reasoning problems are shown by the author and papers he cites to exist in the work of Scientists, Medical Professionals and Psychologists. He presents an excellent set of recommendations of educational systems and for best practices in research in the last chapter entitled: "What Can Be Done". I highly recommend this humorous and thoroughly researched guide to anyone who must evaluate Business, Scientific or Professional Conclusions based upon Statistics. It is even more important to those who write or must reach conclusions themselves.
--Ira Laefsky, IT Consulant MS Engineering/MBA and retired from the Senior Consulting Staff of Arthur D. Little, Inc. and Digital Equipment Corporation
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