This course is a thorough introduction to design of biological studies and statistical analysis in biology. The focus is on the use of statistical models for analyzing biological patterns and processes. Students are taught fundamental skills in modern biological research through project work, exercises and computer exercises. The statistical environment R is used throughout the course.
STK1000 – Introduction to Applied Statistics or equivalent.
Recommended previous knowledge
A background in elementary programming equivalent to the content of BIOS1100 – Introduction to computational models for Biosciences is strongly recommended.
Other recommended background courses are
- BIOS1110 – Celle- og molekylærbiologi (Molecular and Cell biology)
- BIOS1120 – Fysiologi (Physiology)
- BIOS1140 – Evolusjon og genetikk (Evolution and genetics)
- BIOS2100 – General Ecology
In addition to fulfilling the Higher Education Entrance Qualification, applicants have to meet the following special admission requirements:
- Mathematics R1 (or Mathematics S1 and S2) + R2
And in addition one of these:
- Physics (1+2)
- Chemistry (1+2)
- Biology (1+2)
- Information technology (1+2)
- Geosciences (1+2)
- Technology and theories of research (1+2)
The special admission requirements may also be covered by equivalent studies from Norwegian upper secondary school or by other equivalent studies (in Norwegian).
After completing this course, you are expected to:
- Understand the difference between observational studies and experiments, and be able to assess the results from different types of studies in a biological context
- Understand the importance of the terms pseudoreplication, confounding effects, experimental control, randomization, sampling skewness, stratified sampling and blocking in analysis of biological studies
- Be able to carry out Monte Carlo simulations to assess different study design and statistical models
- Be able to fit biologically relevant models based on the normal, binomial, and Poisson distributions (GLM), and calculate linear contrasts and predictions with confidence intervals, as well as evaluate how well these models fit the data (goodness of fit).
- Know how to fit hierarchical models with normally distributed response variables and interpret these
- Know how to assess the sources of bias in models fitted to biological data, including the effects of sampling skewness, measurement error in the predictive variables (attenuation) and loss of study units during the course of the study.
- Be able to present the scientific results of biological studies in written English, as well as gain experience in working with fellow students and academic staff on research projects