23rd Summer Institute in Statistical Genetics (SISG)

Module 1: Probability and Statistical Inference

Session 1: Mon Jul 9 to Wed Jul 11
Instructor(s):

Module dates/times: Monday, July 9; 8:30 a.m. -5 p.m.; Tuesday, July 10, 8:30 a.m.-5 p.m., and Wednesday, July 11, 8:30 a.m.-Noon

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.

Also offered as part of the Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID 2018).

Jim Hughes is Professor of Biostatistics at the University of Washington. He is interested in the application of statistical methods to problems in AIDS and other sexually transmitted diseases. He is particularly interested in cluster randomized trial designs and statistical methods for dealing with misclassified data. He is heavily involved in graduate and undergraduate teaching and graduate student advising, and he has won teaching awards. He recently published “On the design and analysis of stepped wedge trials.” Contemporary Clinical Trials. 45(Pt A):55-60, 2015.

Amy Willis is Assistant Professor of Biostatistics at the University of Washington. Amy’s research is concerned with statistical models for the microbiome. She is especially interested in modeling microbial diversity and detecting shifts in ecologies. She is also interested in mathematical phylogenetics, compositional data analysis, statistical inference, and applied statistics. Her most recent publication is “Uncertainty in phylogenetic tree estimates,” Journal of Computational & Graphical Statistics, 2017.

 

Access 2017 Course Materials (2018 materials will be uploaded to this page prior to the start of the module)