Welcome to Biostatistics for Laboratory Scientists
Long Title: Introductory Statistics for Laboratory Scientists
Course Units/Hours (Virtually): 2 credit hours; course meets Tuesday/Thursday 11:00a – 12:15p*
Grading Basis: (GRAD – H, P, L, F)
Course Component (lecture or lab): Lecture/discussion
Course Format: Synchronous zoom lectures on statistical concepts, Asynchronous Panopto Tutorials, & Software workshops
Course Director: Dr. Stephanie Gupton, PhD (sgupton@unc.edu)
Instructor: Dr. Sara Faccidomo, PhD (sara_faccidomo@med.unc.edu)
Office Hours (Zoom): Tuesdays after class (12:15-1p) and by appointment
Course Description: BBSP 710 introduces basic concepts of statistics in experimental biological sciences to 2nd year+ graduate students. Emphasis is on mastery of common statistical skills and familiarity with advanced analytical skills with emphasis on trying out a variety of statistical and graphical software and coding options. Sample topics include experimental design, hypothesis testing, inferential statistics, power, correlation, and regression. No previous background in statistics is required, but access to multiple devices and a stable internet connection is required.
Course Objectives: The objectives of this course are to provide graduate students in biomedical research programs familiarity with proper experimental design and basic biostatistics concepts, and introduce students to the wide variety of software options for statistical analysis and graphing. By the end of the course, students should understand the principles of experimental design, be familiar with basic statistical methods (and how they are applied), know how to graph and analyze data sets similar to those produced in their thesis laboratory, run a power analysis for experimental design and grants, & learn how to conduct an effective peer-review, focused on evaluation of experimental design and results.
Learning Objectives: By the end of the course, participants will be able to:
- Demonstrate basic understanding of the conceptual background behind statistical tests and assess appropriateness of for chosen analysis data sets (participation, tutorials)
- Apply foundational experimental principles and statistical knowledge to design experiments that address the research questions (participation, tutorials, final project)
- Select applicable statistical tests to perform analyses, interpret results, and represent statistical findings accurately (tutorials, final project)
- Author effective statistical methods, results, and discussion sections (display results effectively in text and graphically) (tutorials, final project)
- Learn how to conduct a priori and post-hoc power analyses for rigorous experimental design and grant submissions (tutorial, lecture, final project)
- Evaluate study design, analysis, and interpretation by providing constructive feedback through the peer-review process (final project, peer review)
Course Grading Policy: a) Participation & Practical Applications (30%); b) Homework Assignments (40%); and c) Final Project (30%).