Course Description
Introduces foundations of statistical analysis in the context of Microbiological applications. Types of data distribution patterns, corresponding statistical methods, data normalization approaches, and P-values will be presented. Online and licensed software resources will be discussed.
Athena Title
Stat Quantitative Microbiol
Non-Traditional Format
We seek to offer this course as a 1-credit short course, to meet during the last 5 weeks of the semester. It is a highly focused introduction that is geared specifically toward Microbiology graduate students.
Semester Course Offered
Offered fall
Grading System
S/U (Satisfactory/Unsatisfactory)
Course Objectives
This course provides foundational information and experience across statistical methods to provide students with a basic understanding of statistical approaches. Students who complete this course will be able to explain the basis for statistical tools that are appropriate for major types of data sets in Microbiology and will be able to make informed choices regarding the critical evaluation of their own data.
Topical Outline
The major types of data distribution patterns; i.e., normal vs. distorted (math and biological data examples) The appropriate statistical methods for each distribution type (reasons, examples) Statistical methods appropriate for situations when there is no information about the data distribution pattern (examples) Different approaches for data normalization (particularly large data sets like RNA seq); Pros and cons of each P-values, false discovery rates, power analysis, and principal component analyses (rigor and reproducibility) Survey of online and licensed software resources available to facilitate calculations described by the statistical theory
Syllabus