BIOL 6764 Biological Data Analysis
I teach this course every spring semester. It’s designed for graduate students in biology who are conducting original studies and applying statistical models to data in their thesis work. It is also for students who want to improve their understanding of statistical models reported in the literature. The focus is not so much on mathematical calculations (though we do consider some intermediate math), but rather on the process of how to think like a statistician or data analyst. Students learn how to choose and evaluate appropriate quantitative models and how to interpret and write about those aspects of a biological study. They also learn how to carry out the computation necessary for a several different analyses and how to generate publication-ready figures using R. I assume that students in the course have had at least one semester of introductory applied statistics, and at least recognize the terms ANOVA and regression. I also assume that students are NOT able to define a p-value.
The goals for this course are for students to
(1) Become proficient probabilistic thinkers
(2) Learn how to ask good data-based questions and what to measure
(3) Understand how to match data models with science problems
(4) Recognize poor model performance and take corrective action
(5) Become operationally competent using R for statistical computation and graphing