Module dates/times: Monday, July 13; Tuesday, July 14, and Wednesday, July 15.
This module serves as an introduction to statistical inference using tools from mathematical statistics and probability. It introduces core elements of statistical modeling, beginning with a review of basic probability and some common distributions (such as the binomial, multinomial, and normal distributions). Maximum likelihood estimation is motivated and described. The central limit theorem and frequentist confidence intervals are introduced, along with simple Bayes methods.
We then cover classical hypothesis testing scenarios such as one-sample tests, two-sample tests, chi-square tests for categorical data analysis, and permutation tests. The course concludes with an overview of resampling methods, such as the bootstrap and jackknife, and a discussion of multiple testing corrections such as false discovery rate control. This module serves as a foundation for almost all of the later modules.
Training in calculus is not a prerequisite for this module, but a willingness to attempt math problems and some comfort with basic algebra will be necessary.
Access 2019 course materials.