Statistics Done Wrong

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.

“A surprisingly entertaining read. If you feel like you have a decent handle on basic statistics, but wouldn’t trust yourself to set up your own analysis or experiments, you’ll certainly gain something from Statistics Done Wrong.”—Michael Kohl, Citizen428.blog"A bold book, and a fascinating one at that...truly enjoyable, and will forever change the way you view statistics."—Ben Rothke, information security professional "A delightful and informative guide...a compilation of clarity."—John A. Wass, Scientific Computing"A spotter's guide to arrant nonsense cloaked in mathematical respectability."—Gord Doctorow, Boing Boing"Anyone evaluating published research, or anyone analyzing data, must understand these topics to understand (or do) science properly. This book is highly recommended."—Harvey Motulsky, author of Intuitive Biostatistics"I will certainly recommend the book to others who have an interest in medical statistics and indeed to medics who have a dislike of statistics!"—Dr. Catey Bunce, Lead Statistician at Moorfields Eye Hospital NHS Foundation Trust"I ADORE THIS BOOK and plan on sharing it with many of my students...amazing."—Dr. Nicole Radziwill, Assistant Professor, Department of Integrated Science & Technology, James Madison University"Good for newsrooms to have copies of this."—Raju Narisetti, SVP, Strategy, @NewsCorp"A well-written, funny, and useful guide to the most common problems in statistical practice today."—Civil Statistician"The kind of study guide that I think could benefit almost anyone slogging through a statistical analysis for the first time."—Joseph Rickert, Revolution Analytics"I wish every doctor could read this."—Eric LaMotte, MD, University of Washington Internal Medicine Residency Program"A great read...I would recommend it to any scientist exposed to statistics as well as to any non-scientist who would like to understand more about what may lie behind the ‘statistically significant’ results from studies reported in the media every day."—Ulla Sovio, Senior Research Associate, Applied Medical Statistics, University of Cambridge"Anyone who wants a chance at understanding research findings should consider this book as an invaluable guide to getting it right."—Sandra Henry-Stocker, IT World"An important addition to any data scientists library. In addition, the pithy writing style will keep your interest and fuel your creativity for future projects. Highly recommended."—insideBIGDATA"In the world of Big Data and Risk Analytics hype, Statistics Done Wrong is refreshing well grounded and kinda fun."—Erik Heidt, Gartner“Yes, it's brilliant writing and guaranteed to keep you awake for the important lessons. This is the best book I've read on the subject. Highly recommended!”—Adam Tornhill, programmer, psychologist, Lisp hacker, and author“A small but important book. It should be required reading for all scientists, especially editors of journals and officials of funding agencies (not to mention science journalists — well, all journalists).”—Tom Siegfried, Managing Editor, Science News"Right up there with classics like How to Lie With Statistics."—Cory Doctorow, sci-fi author"Overall, this book is a book that meets people where they're at . . . It’s not overly complex, or overwhelming to new learners. It has a clear audience: people who might make these statistical errors and those who might consume these statistical errors. . . . it knows its audience and makes deliberate choices with that audience in mind."—Chelsea Parlett Pelleriti, Ph.D., Chapman University

• IntroductionChapter 1: An Introduction to Statistical SignificanceChapter 2: Statistical Power and Underpowered StatisticsChapter 3: Pseudoreplication: Choose Your Data WiselyChapter 4: The p Value and the Base Rate FallacyChapter 5: Bad Judges of SignificanceChapter 6: Double-Dipping in the DataChapter 7: Continuity ErrorsChapter 8: Model AbuseChapter 9: Researcher Freedom:Good Vibrations?Chapter 10: Everybody Makes MistakesChapter 11: Hiding the DataChapter 12: What Can Be Done?NotesIndex