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 most of us don’t know how to do statistics.

*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!

- 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