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STATISTICS DONE WRONG¶


THE WOEFULLY COMPLETE GUIDE¶

by Alex Reinhart

If you’re a practicing scientist, you probably use statistics to analyze your
data. From basic t tests and standard error calculations to Cox proportional
hazards models and propensity score matching, we rely on statistics to give
answers to scientific problems.

This is unfortunate, because statistical errors are rife.

Statistics Done Wrong is a guide to the most popular statistical errors and
slip-ups committed by scientists every day, in the lab and in peer-reviewed
journals. Many of the errors are prevalent in vast swaths of the published
literature, casting doubt on the findings of thousands of papers. Statistics
Done Wrong assumes no prior knowledge of statistics, so you can read it before
your first statistics course or after thirty years of scientific practice.

If you find any errors or typos, or want to suggest other popular
misconceptions, contact me. If you find this website useful, consider buying the
book! Or find it in Deutsch, 한국어, Italiano, 中文 (简体 and 繁體), or 日本語.

> “Of all the books that tackle these issues, Reinhart’s is the most succinct,
> accessible and accurate assessment of the statistical flaws that render many
> scientific studies suspect… It should be required reading for all scientists”
> 
> —Science News

> “If you analyze data with any regularity but aren’t sure if you’re doing it
> correctly, get this book.”
> 
> —FlowingData


CONTENTS¶

 * Introduction
   * Changes
   * Contact
   * Acknowledgements
   * Copyright note
 * An introduction to data analysis
   * The power of p values
 * Statistical power and underpowered statistics
   * The power of being underpowered
   * The wrong turn on red
 * Pseudoreplication: choose your data wisely
 * The p value and the base rate fallacy
   * The base rate fallacy in medical testing
   * Taking up arms against the base rate fallacy
   * If at first you don’t succeed, try, try again
   * Red herrings in brain imaging
   * Controlling the false discovery rate
 * When differences in significance aren’t significant differences
   * When significant differences are missed
 * Stopping rules and regression to the mean
   * Truth inflation
   * Little extremes
 * Researcher freedom: good vibrations?
 * Everybody makes mistakes
 * Hiding the data
   * Just leave out the details
   * Science in a filing cabinet
 * What have we wrought?
 * What can be done?
   * Statistical education
   * Scientific publishing
   * Your job
 * Conclusion
 * Bibliography

Index


THERE'S A BOOK!

The revised and expanded Statistics Done Wrong, with three times as many
statistical errors and examples, is available in print and eBook! An essential
book for any scientist, data scientist, or statistician.

Buy it!

(or use Amazon, IndieBound, Book Depository, or BN.)

(or Deutsch, 한국어, Italiano, 中文 (简体), 中文 (繁體), 日本語.)


TABLE OF CONTENTS

 * Introduction
 * An introduction to data analysis
 * Statistical power and underpowered statistics
 * Pseudoreplication: choose your data wisely
 * The p value and the base rate fallacy
 * When differences in significance aren’t significant differences
 * Stopping rules and regression to the mean
 * Researcher freedom: good vibrations?
 * Everybody makes mistakes
 * Hiding the data
 * What have we wrought?
 * What can be done?
 * Conclusion
 * Bibliography


QUICK SEARCH





NAVIGATION

 * Next chapter: Introduction


Statistics Done Wrong by Alex Reinhart is licensed under a Creative Commons
Attribution 4.0 International License.
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