Ecological methodology: Study design and statistical analysis

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.

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.

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.

Recommended prerequisites

BIO-2004 Study design and data analysis in Biology

If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:

• BIO-3123 Ecological methodology: Study design and statistical analysis 10 ects
• FSK-3006 Model theory and data processing methods 3 ects
• BIO-8105 Ecological methodology: Study design and statistical analysis 10 ects

Language of instruction and examination

Schedule

This course is open to incoming exchange students.

Study Level: Master's

This course has admission prerequisites, which are listed under the Admission requirements section. Please review this information carefully before adding the course to your Learning Agreement.

For details on how to apply for exchange, course selection guidelines, or to contact the Incoming Admissions Team, please visit: Admissions for Student Exchange.

More info about the coursework requirements

Two home-assignments with practical exercises in R (Approved/not approved).

Approved work requirements are valid for three years.

Re-sit examination