NBIK14016U Experimental Design and Statistical Methods in Biology (StatBio)

Volume 2024/2025

MSc Programme in Biochemistry
MSc Programme in Biology
MSc Programme in Environmental Science


The course is intended to give a broad overview of experimental designs and statistical methods in order for students to plan their own experiments and to analyze existing data. Students are recommended to follow the course shortly before they start their bachelor project, their M.Sc. thesis project, or as a part of a Ph.D. study programme.


Learning Outcome


The student will get an overview of a range of statistical concepts and tools:

  • Regression
  • Interaction between factors
  • Design considerations
  • Model check and fit, data representation
  • Systematic and random effects
  • Generalized Linear Models
  • Contingency tables
  • Use of statistical software (SAS and R)


A student that has successfully finished the course will possess the following qualifications:

  • be able to select the appropriate statistical model for the design in question.
  • be able to apply General Linear Models (analysis of variance (ANOVA), analysis of covariance (ANCOVA), polynomial and multiple regression, nested and mixed ANOVAs).
  • be able to apply multivariate ANOVA (MANOVA), repeated measurements ANOVA, and log-linear models.
  • be able to apply Generalized Linear Models (with non-Gaussian error distributions, including logistic regression).
  • be able to summarize and analyze data in contingency tables.
  • be able to construct statistical models that incorporate qualitative (both fixed and random effects) and quantitative variables, main effects, interactions, and second or higher order terms.
  • be able to use statistical software (SAS and/or R) to load a data set, to sort and summarize data, to perform relevant statistical analyses, and to report the results either graphically or in tables.
  • be able to estimate the parameters of a statistical model and their standard errors, and to test whether they are significantly different from 0.
  • be able to identify significant and non-significant factors so as to simplify statistical models using various criteria for best fit.
  • be able to apply a priori and a posteriori tests to identify treatment differences.
  • be able to use statistical models as a predictive tool to forecast the expected outcome of an observation from a set of independent variables.
  • be able to check whether data meet the assumptions of the model and, if needed, to select an appropriate data transformation or another type of model.
  • Be able to use Principal Component Analysis for variable reduction and visualisation of multivariate data.


The student will learn the most commonly used experimental designs and appreciate their advantages with respect to the subsequent statistical analysis of data. The student will be able to construct a statistical model for the experimental design, state the relevant statistical hypotheses, conduct the statistical analysis (generally using statistical software), present the results in a clear and understandable way, and finally interpret the results in a biological context to reach a sound conclusion based on the empirical evidence. In addition, the student should possess the necessary theoretical insight in statistics to be able to understand and comment critically on the use of statistics by others.

See Absalon.

The students are assumed to possess a basic knowledge of statistics at a level corresponding to at least “Matematik/Statistik” (1st year of bachelor study). The time schedule does not allow for repetition of basic statistics, so students lacking an up-to-date knowledge are requested to refresh fundamental statistical concepts and principles prior to the course. Although the course puts emphasis on applying statistics, it is unavoidable that some theory will be encountered, so students with poor mathematical skills should consider whether the course fulfils their needs.

Academic qualifications equivalent to a BSc degree is recommended.
Lectures: 36 hours (2+2 hours per week for 9 weeks, of which c. 1 hour per week is student presentations). Computer exercises: 27 hours (3 hours per week for 9 weeks).
  • Category
  • Hours
  • Lectures
  • 24
  • Class Instruction
  • 12
  • Preparation
  • 129
  • Practical exercises
  • 27
  • Project work
  • 14
  • Total
  • 206
Continuous feedback during the course of the semester

Individual feedback to student presentations. Written and/or oral feedback to homework exercise reports.

7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
The continuous evaluation is based on four sub-parts:
1) During the course the student has to give a short (12-15 minutes) presentation of a case study that has involved statistical analysis. The study may either be taken from literature or be from one of the students' own projects.
2) Students should, in groups of 2 or 3 hand in three homework exercises of an acceptable standard.
3) The student will be evaluated on the participation during the course (at least 80% attendance at student presentations and exercises required).
4) The student will be evaluated on the quizzes in connection with the lectures.

The evaluation is based on an overall assessment of the four sub-parts.
All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
One internal examiner
  1. The submission of completed exercises, with themes provided by the lecturers 5 days before the examination, of a sufficiently high standard to demonstrate understanding of the field.
  2. An individual oral presentation of 10 minutes duration and 10 min questioning, of relevant aspects of experimental design and statistical analysis in a scientific paper chosen by the teachers, with a 3 day preparation period (all aids allowed during the exam).

The response to each part of the re-examination (presentation+ exercises) should be of sufficient standard in order to pass.


Criteria for exam assesment

See Learning Outcome.