Kevin Potter (updated 08/01/2021)

This website provides a series of tutorials and FAQs for statistics, giving helpful details, examples, and reminders for the applied researchers. Examples will focus on psychology research and use (primarily) the freely available statistical software R (for details on using R, see this website for tutorials and further links). Both Frequentist and Bayesian approaches will be covered (though note the author is biased toward the Bayesian approach).

Sections

• Probability
• Estimation
• Testing simple hypotheses
• Linear regression
• Generalized linear models
• Hierarchical/Multi-level modeling
• Advanced techniques

Additional resources

Below are some useful, more in-depth resources for learning statistics and statistical theory:

• The textbooks Probability and Statistics by DeGroot and Schervish and Statistical Inference by Casella and Berger provide a thorough detailing of the foundations of statistics.
• A mid-level textbook to consider is An Introduction to Statistical Learning: with Applications in R by James, Witten, Hastie and Tibshirani, intended as an accessible textbook for those in the non-mathematical sciences.
• The more theory-intensive Elements of Statistical Learning by Hastie, Tibshirani and Friedman (2009), for those wanting an in-depth and strong theoritical background with applicability to machine-learning methods.
• For an accessible introduction to Bayesian approaches and modeling, see Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan by Kruschke, or Statistical Rethinking by McElreath.