NBIA08011U Statistics for Molecular Biomedicine

Volume 2024/2025

MSc Programme in Biology
MSc Programme in Molecular Biomedicine
MSc Programme in Environmental Science


The course is an introduction to statistics aimed for students of medical and biological sciences. An important part of the course is to learn the practical application of statistics using R, which is widely used open source programming language for statistics and data analysis.

Lectures will be a mixture of theoretical parts as well as pratical elements with emphasis on hands-on analysis in R.  

Topics include:

  • Introduction to the statistical program R
  • Descriptive statistics
  • Probability and probability distributions
  • Study design
  • Hypothesis testing/ interval estimation
  • Non-parametric methods
  • Analysis of variance
  • Linear regression and multiple linear regression 
  • Logistic regression
  • Lectures will 
Learning Outcome


The student will obtain knowledge of

  • Statistics for data of biological and/or medical relevance, particularly in the context of the above listed topics. 
  • The symbolic language of statistics and the corresponding formalism
  • Interpretation of statistical results for experimental data
  • The statistical programming language R (combined with the optional but highly recommended RStudio)


  • Set up statistical models for data of biological and/or medical relevance – taking as a starting point models based on the binomial and normal distributions.
  • Handle the symbolic language of statistics and the corresponding formalism
  • Perform significance testing, p-value calculation and interpretation for simple experimental data, including compute-intensive techniques such as permutation testing.
  • Report the results of model set up, data analysis, interpretation and assessment.
  • Use R in order to produce basic visualisations, summary statistics and for carrying out necessary calculations of statistical tests for the analysis of biological data. 


  • Formulate scientific questions in statistical terms.
  • Carry out the necessary calculations using R. 
  • Interpret and report the conclusions of a practical statistical analysis.
  • Assess and discuss a statistical analysis in a biomedical context.

See Absalon.

Academic qualifications equivalent to a BSc degree is recommended.
Lectures and interactive exercises in R, with some supportive flipped elements (at home videos/reading) to provide room for more focus on practical work in sessions. Students are expected to complete regular exercises throughout the course, either individually or in groups.
The course will involve interactive R sessions, so students will need to bring a laptop computer to lectures. We strongly recommend having R or RStudio installed prior to the first session.
  • Category
  • Hours
  • Lectures
  • 35
  • Preparation
  • 101
  • Practical exercises
  • 40
  • Exam
  • 30
  • Total
  • 206
7,5 ECTS
Type of assessment
Oral exam on basis of previous submission, 20 minutes (no preparation time)
Type of assessment details
During the oral exam, the student is expected to be able to explain how they arrived at their solutions, as well as answer general questions about the various topics of the course.
The oral exam is without preparation time.

The final course grade will be determined on the basis of the oral exam.
Exam registration requirements

The written assignment should be completed on an individual basis and must be approved in order to go to the oral exam.

Only certain aids allowed
  • It is allowed to use Large Language Models (LLM)/Large Multimodal Models (LMM) – e.g. ChatGPT and GPT-4 for the written assignment, but the student must clearly state in their solutions how the have used this software.
  • The oral exam is without any aids. 
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

Oral examination, 20 minutes with 20 minutes preparation time.

Notes, books and digital books are allowed during preparation.

Criteria for exam assesment

In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.