Principles of Biostatistics I

4 credits · Graduate School

This course is the first in a two-course sequence on fundamental principles of biostatistics. Students will learn the statistical principles that govern analysis of data in biomedical research, focusing on principles of statistical inference and their application to data analysis techniques. Students will learn how to summarize and interpret data and will learn how statistical tools are used toward evidence-based decision making. Topics include principles of probability, common distributions and their characteristics, bias and variability, introduction to large sample theory (e.g., the law of large numbers and the central limit theorem), estimation principles for means and proportions, an introduction to nonparametric statistics, the basics of simulation studies, and an introduction to resampling techniques (e.g., bootstrapping). The frequentist, Bayesian, and likelihood paradigms for statistical inference are all introduced. This course emphasizes implementation and interpretation over theory and computation. This course is co-located with and serves as repeat credit for BIOS 6311. [4]