Statistics Done Wrong: The Woefully Complete Guide

by Alex Reinhart

Overview

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.

Product Details

ISBN-13:	9781593276201
Publisher:	No Starch Press
Publication date:	03/01/2015
Pages:	176
Product dimensions:	5.90(w) x 8.90(h) x 0.70(d)

About the Author

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.

Table of Contents

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

Editorial Reviews

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

From the Publisher