In this module, you will learn basic concepts relevant to the design and analysis of experiments, including mean comparisons, variance, statistical significance, practical significance, sampling, inclusion and exclusion criteria, and informed consent. You’ll also learn to think of an experiment in terms of usability, its participants, apparatus, procedure, and design & analysis. This module covers lecture videos 1-2.

In this module, you will learn how to analyze user preferences (or other tallies) using tests of proportions. You will also get up and running with R and RStudio. Topics covered include independent and dependent variables, variable types, exploratory data analysis, p-values, asymptotic tests, exact tests, one-sample tests, two-sample tests, Chi-Square test, G-test, Fisher’s exact test, binomial test, multinomial test, post hoc tests, and pairwise comparisons. This module covers lecture videos 3-9.

In this module, you will learn how to design and analyze a simple website A/B test. Topics include measurement error, independent variables as factors, factor levels, between-subjects factors, within-subjects factors, dependent variables as responses, response types, balanced designs, and how to report a t-test. You will perform your first analysis of variance in the form of an independent-samples t-test. This module covers lecture videos 10-11.

In this module, you will learn about how to ensure that your data is valid through the design of experiments, and that your analyses are valid by understanding and testing for their assumptions. Topics include how to achieve experimental control, confounds, ecological validity, the three assumptions of ANOVA, data distributions, residuals, normality, homoscedasticity, parametric versus nonparametric tests, the Shapiro-Wilk test, the Kolmogorov-Smirnov test, Levene’s test, the Brown-Forsythe test, and the Mann-Whitney U test. This module covers lecture videos 12-15.