Table of contents

The course is connected to the following study programs

Teaching language

Norwegian/English 

Learning outcomes

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

  • understand principles of study and experimental design in ecological and evolutionary research
  • detect and describe patterns and uncertainty in data using exploratory visualisation and summary statistics
  • build and interpret linear models and generalised linear models
  • evaluate and compare models using appropriate model selection techniques
  • critically review and improve data analysis workflows, including those of others
  • apply statistical methods to their own research questions and datasets
  • structure and document reproducible analysis projects using version control

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

  • import, clean, and reshape complex biological datasets
  • create informative and effective data visualisations
  • implement, interpret, and compare statistical models (LMs and GLMs)
  • organise analyses into transparent, reproducible workflows
  • assess and improve the structure, readability, and efficiency of statistical code
  • adapt statistical methods to novel research questions and non-standard datasets

Contents

This course provides a hands-on introduction to statistical analysis of real-world data in ecology and evolution, with a strong emphasis on reproducibility and good coding practices. Students learn how to design studies, wrangle and visualise biological data using R, and build and interpret linear and generalised linear models. The course also introduces model diagnostics and model selection, as well as workflow documentation using Git and RStudio projects. Students participate in a student-led R coding club, where they collaboratively solve problems, review each other’s code, and practice reproducible workflows in a peer-supported environment. The course lays a strong foundation for more advanced statistical methods covered in follow-up courses.

Teaching and learning methods

The course is taught together with BIO415 and consist of a mixture of lectures, practical exercises, and seminars designed to build required skills for future statistical analyses in ecology and evolution. Teaching will focus on the practical application of statistical methods, with an emphasis on coding in R and implementing reproducible workflows using Git. Instruction will be given in English. More information will be given in Canvas at the start of the course. The estimated student workload in this course is 135 hours.

Examination requirements

Approval of all mandatory assignments, as specified in Canvas.

Examinations

Portfolio graded pass/fail. More information will be given in Canvas at the start of the course.

Student evaluation

The course will be evaluated in accordance with the quality system for education, chapter 4.1.

Reduction of Credits

This course’s contents overlap with the following courses. A reduction of credits will occur if one of these courses is taken in addition:

Course Reduction of Credits
BIO403 – Statistics in Ecological Studies. 5
Last updated from FS (Felles studentsystem) Oct. 15, 2026 7:43:27 AM