BIO415 Study Design, Data Analysis, and Reproducible Science (Autumn 2026)

After successful completion of the course, students will be able to:

• understand common challenges in data collection and data quality
• apply appropriate study designs to ecological and environmental problems
• detect and describe trends in data using summary statistics and graphics
• build and interpret linear and generalised linear models (log-linear and logistic)
• be familiar with mixed, nonlinear, and additive models, and model selection techniques
• explore multivariate responses using ordination and visualisation methods

In doing so, students will develop the following practical skills using R, RStudio, and Git:

• organise and reshape complex datasets
• create informative data visualisations
• implement and interpret statistical models
• analyse multivariate data
• use version control to support reproducible research

The course introduces students to study design and applied statistical methods commonly used in ecology, evolution, and environmental science. Students learn how to organise, manage, analyse, and interpret data using robust and repeatable workflows for data wrangling, visualisation, and statistical modelling. These workflows are implemented using the programming language R, the RStudio environment, and the version control system Git.

The course covers basic linear models and introduces extensions such as generalised linear models, mixed models, and generalised additive models. Students are also introduced to multivariate models and ordination techniques for analysing data with multiple response variables. Different approaches to model selection are discussed, along with methods for visualising and exploring multivariate structure in ecological datasets. Emphasis is placed on understanding how to translate scientific questions into statistical language, build appropriate models, carry out analyses, and validate results.

All topics are introduced and illustrated using practical examples. The course also introduces students to the essentials of programming and script-based data analysis, and highlights best practices for reproducible and ethical research.

The course is aimed at MSc students who need statistical skills for their thesis work, with examples drawn from ecology, evolution, and environmental science. Teaching focuses on the practical application of statistical methods using R, RStudio, and Git. Classes combine lectures with hands-on exercises designed to build the skills needed to understand, perform, and interpret analyses commonly used in the biological sciences and in students’ own research.

Instruction is in English. More details will be provided in Canvas at the start of the course. The estimated student workload is 270 hours.

Portfolio. See Canvas for more information. Graded assessment A-F.

There will not be arranged a postponed exam for the portfolio.