BIOL 373 (Fall)


B. Leung
(514) 398-6460
3 credits (2-2-5)
MATH 112 or equivalent.
The aim of this course is to introduce students to the foundations of the analysis of biological data,  while emphasizing the assumptions behind statistical tests and models. I shall not as in the mathematical statistics course, go into detail about the specific mathematical derivations.  The course is designed to give a student the ability to intelligently use the statistical techniques typically available on computer packages such as SYSTAT or SPSS.

  • Course introduction; Introduction to data presentation
  • Populations and samples; Pseudoreplication; Central tendency and variability
  • Introduction to probability
  • Normal distribution
  • Introduction to hypothesis testing
  • One-sample hypotheses
  • Two-sample hypotheses:  the t-test
  • Non-parametric statistics: Mann-Whitney; Data transformation
  • Paired-sample hypotheses
  • Multi-sample hypotheses: single-factor ANOVA
  • Multiple comparisons
  • Power, sample size, and assumptions in ANOVA; non-parametric ANOVA: Kruskal-Wallis test
  • Two-Factor ANOVA with equal replication
  • Two-Factor ANOVA: theory and multiple comparisons.
  • Two-Factor ANOVA without replication; Randomized block; Repeated measures
  • Hierarchical ANOVA, MANOVA
  • Linear Regression
  • Linear Regression; Hypothesis testing
  • Multiple regression, Polynomial Regression, ANCOVA
  • Goodness of fit: Chi-square
  • Contingency tables
  • Advanced topics

You may not be able to get credit for this course and other statistic courses. Be sure to check the Course Overlap section under Faculty Degree Requirements in the Arts or Science section of the Calendar.
Check out:
1. J.H.Zar. Biostatistical Analysis, 5th ed., 2009. 
2. Sokal, R.R., and F.J. Rohlf. 1995.  Biometry, 3rd ed., W.H. Freeman & Co. (Not available in book store but can be ordered)
Lectures and labs.

Labs, class assignments, and a final exam.

McGill University values academic integrity. Therefore all students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Code of Student Conduct and Disciplinary Procedures (see for more information)

Last update: March 22, 2017