This course will deepen students’ understanding of common statistical procedures used in the health sciences, by presenting them clearly and applying them in a variety of healthcare contexts.
Students will kick off the course with two important units – one on planning studies and analyses, and another on identifying analytical and algorithmic bias. Students will have the opportunity to apply critical thinking skills in a series of weekly discussion posts where they will be able to critique, discuss and debate real-world researchers’ results, presentations and methods by reviewing recent academic research.
Statistical methods reviewed during the course will include probability, distributions, descriptive statistics, t-testing, ANOVA, MANOVA, correlation, regression, survival analysis and a few categorical analysis techniques. Throughout, there will be repetitive application of hypothesis testing, confidence intervals, p-values, risk and odds ratio calculation, and prediction. Students will practice accurately and clearly interpreting and communicating statistical results. Students will also be provided with hands-on exposure to modern methods of applying statistical techniques through interaction with a series of pre-coded Google Collab notebooks written in python.