Course code:
This course will provide students with a toolbox of techniques in statistical analysis, with a focus on the biological sciences. Students will learn how to choose and apply a variety of widely used statistical tests, how to design experiments and studies with statistical analysis in mind, and how to use a range of specialized statistical approaches for data types frequently encountered in the biological sciences. The methods we will cover include parametric and nonparametric tests; approaches designed for categorical, ordinal, and continuous data; biodiversity statistics and ordination methods; Bayesian vs. frequentist inference; and robust experimental design. The class will highlight the assumptions involved in statistical inference and the conditions that must be met in order to use statistical tests appropriately. In the lab, students will use the statistical programming language R to explore, display, and analyze data using the methods covered in class. By the end of the term, students should be able to choose appropriate analytical methods for a wide range of data types, design statistically valid experiments, and write code for basic statistical tests in R. Students will be evaluated based on daily homework assignments, weekly lab work, several take-home exams, and a final group presentation based on an original analysis of an archived data set chosen by the students. Note: each student should have a laptop for lab (PC preferred; limited support will be provided for Mac users). Contact the instructor if you do not have your own laptop.
Prerequisites:
An introductory course in statistics (Intro to Statistics and Research Design, Probability and Statistics, or equivalent).
Always visit the Registrar's Office for the official course catalog and schedules.
