Summary
This course provides an introduction to biostatistical methods. The course emphasizes practical considerations required to design studies, perform elementary analysis, and become an informed consumer of statistical data. Topics include study design, exploratory data analysis, the P value and hypothesis testing, power analysis, and reproducible analysis methods using the R statistical environment.
Introduction
Statistical maturity is a core competency for researchers and health professionals, but formal education in statistical methods and practice varies widely among the graduate schools at UCSF. Although graduate students may opt to take a full introductory course such as Biostat 183, no statistical classwork is required of BMS, Informatics, PSPG, or Tetrad students. Students arrive on a continuum between a complete lack of formal training and having completed Masters level work in statistics. This course will provide a flexible, introductory set of modules that will allow biomedical students to design studies, perform essential analysis, and consume statistical data.
This course will emphasize practical considerations important to a working bench scientist. Mathematical detail and notation will be included where it is essential. The P value is both crucial and widely misunderstood, so the course places particular emphasis on what the P value means and interpreting the P value in the context of effect size and study power.
Course objectives
This course will provide a flexible, introductory set of modules that will allow biomedical students to understand biostatistical methods required to design studies, perform essential analysis, and consume statistical data. This course will emphasize practical considerations important to a working bench scientist. Mathematical detail and notation will be included where it is essential. Upon completion of this course, students will be prepared to:
- describe the advantages and drawbacks of cohort, case-control, and RCT study design
- use and interpret the tools of exploratory data analysis, including histograms, box and whisker plots, and correlation
- calculate a P value, explain how P values are used in hypothesis testing, and adjust P values for multiple tests
- calculate and interpret confidence intervals
- calculate and perform power calculations
- Apply common statistical tests including the t test and Fisher’s exact test
- Perform reproducible statistical analysis using the R language
- Instructor: David Quigley