An interactive guide to the logic and steps of hypothesis testing and to choosing an appropriate statistical technique, with examples from education and psychology.

An interactive, step-by-step take-home on the foundations of statistics with R. It covers importing and preparing data, basic statistical concepts, descriptive statistics, graphs, the normal distribution and standard score transformations. Each section opens after the previous one is answered correctly.

A practical guide to checking the normality assumption in R, with descriptive statistics, z-scores, graphs and formal tests, using the sat.act data from the psych package.

A course note on the normal distribution and standard score transformations. It covers the properties of the normal curve, probabilities in R, and how observed scores are turned into z, T, IQ-type, CEEB, normalized T, stanine and sten scores, with worked examples and exercises.

This class note covers descriptive analysis of a real dataset in R, from loading the data to an APA-style report. Students code along on their own laptops with the generated dataset.

Example for exploratory and confirmatory factor analysis in R with Andy Field's SAQ dataset.