spring 2019

BIO-3012 Studydesign and dataanalysis in Biology II - 10 stp

Sist endret: 01.11.2018

The course is administrated by

Faculty of Biosciences, Fisheries and Economics

Studiested

Tromsø |

Application deadline

Concerns only admission to singular courses: Applicants from Nordic countries: 1 June for the autumn semester and 1 December for the spring semester. Exchange students and Fulbright students: 1 October for the spring semester and 15 April for the autumn semester.

Type of course

Mandatory course for all masterstudents in biology. 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. The course is available as a singular course.

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.

Course contents

Sampling strategies of observational data from biological systems Principles of biological experiments Introduction to statistical modelling of biological data with emphasis on general and generalised linear models. 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 and doctoral students within many fields of biology.

Objective of the course

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. 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. 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

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.

Assessment

Oral exam and project work. Grade: Pass/Fail. There will be a re-sit examination for students that did not pass the previous ordinary examination.

Schedule

Course overlap

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

Recommended reading/syllabus

Syllabus and reading list will be announced prior to course start.  
Lectures Spring 2019
First attandance: see schedule.
Forelesning prof. Nigel Gilles Yoccoz
Datalab Gr. 1 prof. Nigel Gilles Yoccoz
Datalab Gr. 2 prof. Nigel Gilles Yoccoz