About the course | Intended audience | Prerequisites | Content details
About the course
A practical introduction to statistical analysis for researchers working with biological and experimental data.
This course teaches how to choose, run, and interpret common statistical analyses using R or Python. The focus is on practical application rather than statistical theory, with hands-on exercises throughout.
Topics include hypothesis testing, ANOVA, regression, linear models, model selection, and power analysis.
By the end of the course, participants should be able to:
- analyse datasets using standard statistical techniques
- select appropriate statistical tests for different research questions
- understand the assumptions and limitations of common methods
- carry out statistical analyses confidently in R or Python
Teaching is primarily hands-on, with short lectures introducing key concepts and methods.
Back to top Intended audience
This course is suitable for:
- researchers and students working with biological or experimental data
- participants who already have basic familiarity with either R or Python
- anyone looking for a practical foundation in statistical analysis
Prerequisites
If you are completely new to programming, introductory training in R or Python is essential before attending.
Related courses: Data analysis in R or Python, Core statistics
Content details
The course is delivered over six half-day sessions:
- CS1 — Simple hypothesis testing
Introduces the foundations of statistical inference using one-sample, two-sample, and paired tests. Participants learn how to formulate hypotheses, check assumptions, choose appropriate tests, and interpret results using both parametric and non-parametric approaches.
- CS2 — Single categorical predictor variables
Covers statistical analyses involving a single categorical explanatory variable, including one-way ANOVA and non-parametric alternatives such as the Kruskal–Wallis test. The session focuses on comparing groups, interpreting model outputs, and assessing assumptions through visualisation and diagnostic checks.
- CS3 — Single continuous predictor variables
Introduces linear regression and correlation analysis for studying relationships between continuous variables. Participants learn how to build, interpret, visualise, and critically assess simple predictive models using biological and experimental datasets.
- CS4 — Two predictor variables
Explores statistical models containing two explanatory variables, including two-way ANOVA and regression with grouped data. The emphasis is on understanding interactions, controlling for additional variables, and interpreting more complex experimental designs.
- CS5 — Multiple predictor variables
Extends linear modelling to multiple predictors and introduces model comparison and selection techniques. Participants learn how to construct multivariable models, assess explanatory power, and identify appropriate models for complex datasets.
- CS6 — Power analysis
Introduces statistical power and experimental design considerations for planning robust studies. Participants learn how sample size, effect size, and variability influence the ability to detect meaningful biological effects.