Graduate School of health and Medical Sciences

An intro to experimental design and planning of in vivo experiments
Provider: Faculty of Health and Medical Sciences

Activity no.: 3692-24-00-01	There are 8 available seats
	Enrollment deadline: 20/10/2024

Place	Online
Date and time	18.11.2024, at: 09:00 - 22.11.2024, at: 16:00
Regular seats	12
Course fee	7,680.00 kr.
Lecturers	Otto Kalliokoski
ECTS credits	3.50

Contact person	Otto Kalliokoski E-mail address: ohk@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration E-mail address: phdkursus@sund.ku.dk

Aim and content This course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD Students from NorDoc member faculties. All other participants must pay the course fee. Anyone can apply for the course, but if you are not a PhD student at a Danish university, you will be placed on the waiting list until enrollment deadline. This also applies to PhD students from NorDoc member faculties. After the enrollment deadline, available seats will be allocated to applicants on the waiting list. Learning objectives A student who has met the objectives of the course will be able to: 1. Understand and apply principles used to design solid/reproducible experiments 2. Identify some common pitfalls in experimental design and be familiar with ways around them 3. Explain the concept of statistical power, and how it relates to their experiments 4. Understand the principles for calculating appropriate sample sizes for an experiment and be familiar with tools used for the purpose Content The course aims to familiarize the student with the fundamentals of setting up in vivo experiments (animal experiments), coupled with an introduction to the how and why of statistical analysis of results. How do we make statistical analysis of experiments feel like less of a pain? How do we think about statistics before we have started our experiment? The lectures discuss common causes of irreproducible findings and how to mitigate them. Particular emphasis is put on formulating hypotheses; on identifying appropriate designs for answering these hypotheses; on making sure that an experiment is appropriately powered; and on calculating appropriate sample sizes for this experiment. How do we line everything up in such a way as to ensure that we have the best possible odds of obtaining a clear answer from our experiments? Students are encouraged to bring their own experimental designs - a study they will be carrying out, or one that they have done in the past - as the course has a "workshop" element where these designs are analyzed. Can my experiments be carried out in another way? In a better way? Please note that the course is held online in its entirety. Participants The target group for the course is PhD students working independently or as a part of a larger project, who will be setting up or analyzing data from animal experiments. No prior knowledge is required. Relevance to graduate programmes The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH: - All graduate programmes Language English Form Online lectures and exercises (individual- and group work, with exercises and discussions) Course director Otto Kalliokoski, Associate professor, Department of Experimental Medicine, ohk@sund.ku.dk Teachers Otto Kalliokoski Dates November 18-22, 2024 Course location Online Registration Please register before October 20th Expected frequency We will try to provide this course twice a year Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment. Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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