This class presents fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples. Students having taken this class should be able to summarize samples, perform relevant hypothesis tests and perform a collection of two sample comparisons. Classical non-parametric methods and discrete data analysis methods are discussed. The class is taught at a master’s of biostatistics introductory level and requires Mathematical Biostatistics Boot Camp 1 as a prerequisite.
Developed in collaboration with Johns Hopkins Open Education Lab.
Power and sample size and two group tests
Tests for binomial proportions
Two sample binomial tests, delta method
Fisher’s exact tests, Chi-squared tests
Simpson’s paradox, confounding
Retrospective case-control studies, exact inference for the odds ratio
Methods for matched pairs, McNemar’s, conditional versus marginal odds ratios
Non-parametric tests, permutation tests
Inference for Poisson counts
Students should take Mathematical Biostatistics Boot Camp 1 before enrolling in this course. Knowledge of calculus, set theory and a high level of mathematical literacy are prerequisites for this class.
This course consists of video lectures, weekly homework assignments, discussion forums, and weekly quizzes.
Will I get a Statement of Accomplishment after completing this class?
Yes. Students who achieve a sufficient grade will receive a Statement of Accomplishment signed by the instructor.
What resources will I need for this class?
RStudio and R