Type of course

The course is 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, before the course starts.

Admission requirements

Who can apply as a singular course student:

• PhD student enrolled at another institution than UiT. PhD students must upload a document from their university stating that there are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee.
• Holders of a master´s degree of five years or 3+2 years (or equivalent) may be admitted. These applicants must upload a Master´s Diploma with Diploma Supplement / English translation of the diploma. Applicants from listed countries must document proficiency in English. To find out if this applies to you, see the following list: Proficiency in English must be documented - list of countries. For more information on accepted English proficiency tests and scores, as well as exemptions from the English proficiency tests, please see the following document: Proficiency in english - PhD level studies

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 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.
• can decide on which statistical models should be used based on assumptions and data characteristics.
• know how to use generalised 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 of statistical analyses.
• 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