BBSP 710 Biostatistics for Laboratory Scientists
The Office of Graduate Education offers BBSP 710, Biostatistics for Laboratory Scientists.
Note: BBSP 710 is recommended for PhD students in their second year or later. The course asks students to apply statistical methods to their own projects, and first-year PhD students may not have sufficient data to analyze from their rotations.
Course Description: BBSP 710 introduces basic concepts and methods of statistics with an emphasis on applications in the experimental biological sciences to 2nd year graduate students. Emphasis is on mastery of basic statistical skills and familiarity with situations in which advanced analytical skills may be needed, and on mastery of graphing and statistical analysis of data sets using, GraphPad Prism. Topics in statistics include: i) experimental design, ii) description of data, iii) probability, distributions, and confidence intervals, iv) hypothesis testing, v) correlation and regression, and vi) multi-sample interference. Advanced topics include power calculation as well as other topics such as non-linear regression and multiple analysis of variance. The course employs the commercial graphing and analysis program, GraphPad Prism v9.4 (licensing fee required), to graph and analyze data that amplify the concepts presented in the previous lecture. Students should have a basic understanding of algebra and arithmetic. No previous background in probability or statistics is required, nor is experience with statistical computing. Access to a laptop, tablet, or device with video streaming capabilities and a stable internet connection is required.
Course Objectives (Learning Outcomes): The objectives of this course are to provide graduate students in biomedical research programs familiarity with proper experimental design and basic biostatistics concepts for laboratory scientists, and to learn how to use GraphPad Prism for graphing and data analysis. 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), and know how to graph and analyze data sets similar to those produced in their thesis laboratory.
Course Format: Lectures on statistical concepts, Tutorials, & Prism workshops