LMP2004H: Introduction to Biostatistics

Who can attend

A maximum of 20 students can be enrolled in this course.

Ten of these will be from the MHSc in Laboratory Medicine program, while for the remaining 10 spots priority will be given to students from the research streams at the Department of LMP.

Course description

This course introduces the fundamental concepts of Biostatistics, providing an understanding of the basic theoretical underpinnings and practical applications of statistics.

You will learn essential statistical techniques and analyses relevant to your academic studies or professional work in general medicine and in the fields of pathology and clinical embryology.

Course highlights

Basic theoretical underpinnings: An exploration of core statistical theories including probability, distribution, and inference, creating a strong foundation for further study and application.

Practical applications: Instruction on how to apply statistical concepts to real-world problems in general medicine, pathology, and clinical embryology, offering essential skills needed for basic analysis in these fields.

Basics of AI-assisted coding: Introduction to the principles and techniques of AI-assisted coding, integrating artificial intelligence with statistical methods for enhanced data analysis.

Capstone project preparation: The course provides the necessary statistical foundations for students to successfully undertake and complete the program's capstone projects, integrating theory with practical application.

Overall, this course is designed to equip you with the knowledge and skills to navigate the basic concepts of statistics, enabling you to apply these principles.

Learning outcomes

After completing this course, you will be able to:

• understand the foundational statistical concepts including the mean-variance framework, hypothesis testing, and uncertainty principles, in addition to fundamental equations needed for basic statistical analysis.
• understand study design and statistical applications in medical research.
• produce and appraise a methods section in scientific literature, specifically evaluating and appraising the appropriateness of chosen methods and expressing these methods clearly and correctly.
• produce and appraise a results section in scientific literature, specifically evaluating accuracy of reporting, formatting and structure of reporting, and reporting what is described in the methods section accurately.
• evaluate quality of a statistical model, prediction, or test, and explaining these results to a scientific or non-scientific audience.
• understand statistical coding in R and guidance on AI-assisted code generation, editing, and debugging.

Course coordinator

Dr. Sareh Keshavarzi

Teaching Assistant: 

Julia Gallucci
julia.gallucci@mail.utoronto.ca

lmp.grad@utoronto.ca for administrative queries.

Timings and location

Wednesdays 11 am – 2 pm

Location: TF 203, Teefy Hall, St. Michael's College, 59 Queen's Park Crescent East, Toronto, ON M5S 2C4

Office hours - course coordinator: Fridays 2 - 3 pm (virtual)

Office hours TA: Book an appointment

Schedule