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).
"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
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
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
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
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
FAQ for Generalized Linear Mixed Models
Ben Bolker's GLMM FAQ page
