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.

# Statistics

## The Elements of Statistical Learning

The Elements of Statistical Learning: Data Mining, Inference, and Prediction Second Edition, written by Trevor Hastie, Robert Tibshirani and Jerome Friedman, is a valuable resource for statisticians and anyone interested in data mining in science or industry.

## An Introduction to Statistical Learning

An Introduction to Statistical Learning with Applications in R, written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences.

## OpenIntro Statistics: Second Edition

The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. Our inaugural effort is OpenIntro Statistics. Probability is optional, inference is key, and we feature real data whenever possible.

## Lectures on probability theory and mathematical statistics

*Lectures on probability theory and mathematical statistic*s, by Marco Taboga, is freely available online.

## Probability and Statistics Cookbook

**“Probability and Statistics Cookbook”**, by Matthias Vallentin, contains a succinct representation of various topics in probability theory and statistics. It provides a comprehensive reference reduced to the mathematical essence, rather than aiming for elaborate explanations.