Application deadline

Type of course

Mandatory course for all master students in biology. The course is also available as a singular course

It is recommended to have the course BIO-2004 Study designs and data analysis in biology I or an equivalent introductory course of statistics for biologists.

It is also recommended to have knowledge of programming in R. We strongly advise students with no or little experience with programming in R to follow introductory seminars combined with digital courses in R, offered in the first 2 weeks of January.

Admission requirements

Course content

Recommended prerequisites

Objectives of the course

Knowledge

Students who have completed this course

• can explain and provide example for what the different steps in scientific inference are.
• know the differences among the main types of study design (observational vs experimental, randomization, double-blind etc.).
• know the importance of random vs convenience sampling, and how to stratify sampling.
• know the critical assumptions of statistical models such as linear and generalized models, specifically independence and the mean-variance relationship.
• know how to interpret parameters estimated using statistical models, and how to interpret and deal with uncertainty.

Skills

Students who have completed this course

• can design experimental studies to investigate main effects and their interactions.
• can design observational studies, particularly with regard to confounding.
• can decide on which statistical models should be used based on assumptions and data characteristics.
• know how to use generalized linear models (linear regression, ANOVA, ANCOVA, logistic regression, log-linear models) and how to interpret parameter estimates and their uncertainty.
• can organize and analyze data sets using R.

General competence

Students who have completed this course

• are aware of the importance of all steps in the processes of scientific inference, from formulating the biological question, to designing the study, analyzing the data and interpreting the results statistical analysis.
• know the main reasons for choosing different types of studies (experimental, observational) and designs.
• know the importance of assumptions when using statistical models for the robustness of the conclusions, and the relative importance of assumptions (independence, variance-mean relationship, normality, etc.).
• know how to focus on the biological significance and interpretation of parameters rather than statistical significance.
• know how to make research reproducible through the use of scripts with detailed documentation.

Language of instruction and examination

Teaching methods

Information to incoming exchange students

This module is open for exchange students with bachelor degree in Biology that meets the admission requirements.

Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty: https://en.uit.no/education/art?p_document_id=510412