BB839: Planning and evaluation of biological studies (5 ECTS)

STADS: 04013901

Level
Master's level course approved as PhD course

Teaching period
The course is offered in the autumn semester.

Teacher responsible
Email: usteiner@biology.sdu.dk

Additional teachers
jones@biology.sdu.dk
lionel@biology.sdu.dk

Timetable
Show entire timetable
Show personal time table for this course.

Prerequisites:
None.

Academic preconditions:
Students taking the course are expected to:

• Have basic knowledge of statistics and mathematics
• Have a Bachelor’s degree in a field with some level or focus of quantitative methods

Course introduction
The aim of the course is to enable the student to formulate biological questions in order to design, analyse, interpret, and present biological studies, which is important in regard to transition from a student to become a part of the scientific community and for any basic understanding of scientific investigation.

The course builds on the knowledge acquired in the courses Mathematics for Biology, Statistics, and gives an academic basis for studying the topics of statistical analysis and designing studies, that are part of the degree.

In relation to the competence profile of the degree it is the explicit focus of the course to:

• Give the competence to understand quantitative analysis of future projects and their underlying theories 
• Give the competence to enter into academic collaborations and structure personal learning.
• Give skills to use statistical software, program R, for analysis of a variety of models
• Provide knowledge and understanding of scientific theories, experimental methods.

Expected learning outcome
The learning objectives of the course are that the student demonstrates the ability to:

• formulate scientific questions in biological disciplines, e.g. ecology, physiology, neurobiology, and evolutionary biology, in order to test those questions
• design relevant laboratory or field studies in order to test expectations
• handle, analyse, and interpret data from experimental and field studies, including statistical analyses of linear models, ANOVA, ANCOVA, GLM, mixed effect models, and model selection (Information theoretic approaches).
• Select appropriate models for the different types of data and designed studies 
• Present quantitatively the results from biological studies, including graphical representation

Subject overview
The following main topics are contained in the course:

• Deriving questions and hypotheses
• Designing experiments
• Basics of probabilities and distributions
• Linear models and ANOVA
• Model selection and multi model inference
• GLM
• Mixed effect models

Literature

• Hector: The New Statistics with R.

Website
This course uses e-learn (blackboard).

Prerequisites for participating in the exam

1. Prerequisite submission of 80% of the non-graded problem sets using the statistical software of the course. Prerequisite submission of an annotated scientific publication with respect to structure and statistics. Pass/fail, internal marking by teacher. (04013912).

Students will be given the chance of a re-hand in.

Assessment and marking:

1. Written project, which is evaluated by the 7-mark scale, internal marking. All material allowed, that is not plagiarism. (5 ECTS). (04013902).

Reexam in the same exam period or immediately thereafter. The mode of exam at the re-examination may differ from the mode of exam at the ordinary exam.

Expected working hours
The teaching method is based on three phase model.
Intro phase: 24 hours
Skills training phase: 24 hours, hereof:
 - Tutorials: 24 hours

Educational activities

Educational form
Activities during the study phase:

• Exercises (repetition)
• Literature (preparation)
• Annotating literature
• Making a movie on a statistical topic (deeper learning)

Diverse option of teaching methods is offered to match the different demands of students, including lectures, in class exercises, literature, movies, e-learning, research oriented learning, online media.

Language
This course is taught in English.

Course enrollment
See deadline of enrolment.

Tuition fees for single courses
See fees for single courses.