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Vår 2025
BIO-3012 Studydesign and dataanalysis in Biology II - 10 stp
The course is administrated by
Institutt for arktisk og marin biologi
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.
Course overlap
BIO-8105 Ecological methodology: Study design and statistical analysis 10 ects
Course contents
The course aims to demonstrate how biological theory, study designs and analyses should be linked, and the course should make students able to plan and conduct empirical, biological research through all stages of the research processes from formulation of hypotheses to the presentation of the results. The course is based on modules going through the iterative sequence question-design-analyses-inference-reformulated/new question(s), and covering different types of research, such as species distributions, ecotoxicology, diversity patterns and molecular processes. The course is relevant for master students within biology, with a focus on ecology and evolutionary biology.
Admission requirements
Local admission, application code 9371 - Master`s level singular course. Admission requires a Bachelor`s degree (180 ECTS) or equivalent qualification, with a major in biology of minimum 80 ECTS.
Objective 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 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
The language of instruction is English and all of the syllabus material is in English. Examination questions will be given in English, but may be answered either in English or a Scandinavian language.
Teaching methods
Lectures 30 hours and PC-lab 30 hours.