• Home
  • Bio
  • Research
  • Teaching
  • Publications
  • Data Analysis Help
  • Biostats teaching resources
  • Contact

Biostats teaching resources

Some recommended reading
"The Analysis of Biological Data" by Whitlock and Schluter provides an excellent, undergraduate level introduction to data analysis.  Believe it or not, some undergraduates have described it as a 'page turner'!

"Experimental Design for the Life Sciences" by Ruxton and Colegrave.  This excellent and highly-accessible book (written for undergraduates) covers essential features of experimental design.

"Modern Statistics for the Life Sciences" by Grafen and Hails.  An excellent introduction to general linear models (the focus of the materials below); it inspired the approach to teaching topics, below.

"Mixed Effects Models and Extensions in Ecology with R" by Zuur et al. provides a solid introduction to mixed effects models, with code to implement analyses in R.  Although the examples deal with ecology, the principles are universal (and useful for biomedical scientists).
Some seminars that address the current state of biological science
Rigour Mortis:  How bad research is killing science, by Prof. Malcolm Macleod, University of Edinburgh
The Science of Doubt, by Prof Michael Whitlock, University of British Columbia (NOTE:  the proper talk does not start until 11:20)
Materials for BMS2
Introduction to R:  R-Sessions -  The R-Sessions introduce students in BMS2 to R and RStudio;  many additional resources are available on the web to learn R and RStudio, including some options listed, below.

Learn R by using R:  swirls - A alternative resource to learn R (not required for BMS2, but useful)

Data 'wrangling' and visualization:  a free 1 month trial / inexpensive course - yet another resource to learn R (again, not required for BMS2)

Computer workshop:  randomization test

Lecture 1:  philosophy of hypothesis testing & pseudo-replication

Lecture 2:  Understanding variance & plotting data

Lecture 3: How ANOVA works

Lecture 4: ANOVA in practice
Materials for BMS3
Lecture 1:  Review of hypothesis testing & Chi-square tests

Lecture 2: Review of how ANOVA works

Workshop:  Practicing 1-way ANOVA

Lecture 3:  Dealing with assumptions

Lecture 4: 2-way ANOVA & interactions

Lecture 5: regression & ANCOVA

Workshop:  Effect sizes

Workshop:  General linear models

Lecture 6:  Introduction to Mixed effects models
Biomedical science MSc:  introductory data analysis and experimental design
Introductory lecture:  Data analysis basics

Computer workshop:  Introduction to basic statistical tests

Workshop:  Recognizing experimental designs

Workshop:  Power analysis
Graduate level courses in experimental design and data analysis offered elsewhere
An excellent graduate course offered by Prof. Dolph Schluter at the University of British Columbia.  The link includes access to videos of lectures.

A similar course, offering additional materials (e.g., useful publications), by Prof. Leithen M'Gonigle at Simon Fraser University.

For a greater focus on general and generalized linear models, see this excellent course offered by Dr. Jarrod Hadfield at the University of Edinburgh
Bioinformatics
RNA-Seq analysis:  Harvard edX
Meta-Analysis
An online book, offering advice for meta-analysis
Tips for R
Tips for R
FAQ for Generalized Linear Mixed Models
Ben Bolker's GLMM FAQ page
Powered by Create your own unique website with customizable templates.
  • Home
  • Bio
  • Research
  • Teaching
  • Publications
  • Data Analysis Help
  • Biostats teaching resources
  • Contact