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 geospatial kriging systems, 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 swathes 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.

Dive in: the whole guide is available online!

If you find any errors or typos, or want to suggest other popular
misconceptions, *contact me*.

In my quest to build the most comprehensive collection of statistical error
available, I’ve signed a contract to publish *Statistics Done Wrong* as a
**massively expanded book** with new sections on statistical modeling,
additional mathematical explanations, and more detail. Use the box at the right
to sign up to receive updates by email.

- 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