SHUA13011U Statistics and Data Analysis for Human Biologists
Volume 2017/2018
Education
MSc Programme in Human Biology - compulsory
Content
The course participants will be introduced to the fundamentals of statistical reasoning. They will learn how to apply appropriate statistical methods for common laboratory experiments, clinical trials and observational studies. During the course the student is required to work on an assignment(s) that will be handed in and presented on the last day of the course.
Learning Outcome
After completing the course the student is expected to:
Knowledge
- describe the role of descriptive statistics and refer to appropriate statistical summary measures for different types of data
- explain the mechanism of statistical inference, and refer to confidence interval and p-value for drawing evidence-based conclusions
- rephrase statistical significance and statistical power in the context of a given laboratory experiment
- understand the basic idea of how to design and carry out a study where data are collected and conclusions are based on statistical analysis of the data
- reflect on limitations of the conclusions obtained with respect
to study design, measurement error and sample size
Skills
- planning and design of an experimental study
- communicate results of statistical analysis
- present subject matter hypotheses and data
- argue for the selected statistical methods
- discuss the statistical results
Competencies
- critically review public reports
- review all steps from study design, data collection to subject matter conclusion
- learn from the limitations of the own study to motivate follow-up studies
Literature
- Lecture notes published in Absalon
- Introduction to Statistical Data Analysis for the Life Sciences Claus Thorn Ekstrom, Helle Sørensen, 2010 Taylor and Francis
Recommended Academic Qualifications
Note that the course is
developed specifically for human biologists. This means that the
examples used for illustration and the homework assigment may be
difficult to follow for non-biologists.
Teaching and learning methods
Lecturers and
practicals.
Remarks
Statistics and Data
Analysis for Human Biologists, 2013-curriculum, is also open for
students following the 2011-curriculum.
Workload
- Category
- Hours
- Class Instruction
- 15
- Lectures
- 15
- Preparation
- 39
- Total
- 69
Feedback form
Collective
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Exam (Course attestation)
- Credit
- 2,5 ECTS
- Type of assessment
- Course participationApproval of assignment including oral presentation
Participation in minimum 80% of lectures and training activities - Aid
- All aids allowed
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
One internal examiner
Criteria for exam assesment
To achieve a course certificate, the student must be able to:
Knowledge
- describe the role of descriptive statistics and refer to appropriate statistical summary measures for different types of data
- explain the mechanism of statistical inference, and refer to confidence interval and p-value for drawing evidence-based conclusions
- rephrase statistical significance and statistical power in the context of a given laboratory experiment
- understand the basic idea of how to design and carry out a study where data are collected and conclusions are based on statistical analysis of the data
- reflect on limitations of the conclusions obtained with respect
to study design, measurement error and sample size
Skills
- planning and design of an experimental study
- communicate results of statistical analysis
- present subject matter hypotheses and data
- argue for the selected statistical methods
- discuss the statistical results
Course information
- Language
- English
- Course code
- SHUA13011U
- Credit
- 2,5 ECTS
- Level
- Full Degree Master
- Duration
- 5 days in block 4
- Placement
- Block 4
- Schedule
- See Syllabus
- Course capacity
- 40 participants
- Study board
- The Study Board for Human Biology and Immunology
Contracting department
- Department of Public Health
Course Coordinators
- Thomas Alexander Gerds (3-7c696f486a71777b7c697c36737d366c73)
Studiesekretær: Jane Siig,
jasi@sund.ku.dk
Saved on the
22-05-2018