What Stats Should I Run - Stats for biomedical researchers using R in Google Colab
Graduate School of Health and Medical Sciences at University of Copenhagen
This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH.
Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until the enrollment deadline. After the enrollment deadline, available seats will be allocated to the waiting list.
The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee.
Learning objectives
A student who has met the objectives of the course will be able to:
1. Translate a biological question and experimental design into an appropriate statistical model
2. Perform common biomedical statistical analyses in R using Google Colab
3. Interpret the main quantities produced by statistical models
4. Critically evaluate whether an analysis is appropriate
5. Visualise, interpret and report statistical analyses clearly and reproducibly
Content
This 3-day course gives biomedical PhD students a practical and conceptually grounded approach to statistical analysis. Rather than teaching statistics as a catalogue of tests, the course starts from the biological question, experimental design and structure of the data, and then asks which statistical model is appropriate.
Students will work with real biomedical examples and hands-on R notebooks in Google Colab to perform and interpret common analyses including t-tests, ANOVA/ANCOVA, correlation, regression and linear mixed-effects models for repeated, nested and hierarchical data. Particular attention will be given to identifying the correct experimental unit and avoiding pseudoreplication; for example, when multiple cells, tissue sections or repeated measurements originate from the same animal.
The emphasis is on understanding what the analysis is doing and what its output means; including estimates, degrees of freedom, t- and F-statistics, effect sizes, confidence intervals, R² and p-values, rather than simply obtaining a statistically significant result.
The course will also address assumptions, sample-size planning and power, multiple comparisons, inappropriate normality testing, researcher degrees of freedom and other common sources of misleading or irreproducible conclusions. Students will learn how to visualise their data, assess their analysis, interpret results biologically, and report statistical methods and results clearly in manuscripts and PhD theses.
Students will be asked to prepare their own datasets so they can apply directly the methods learned.
Participants
This course is primarily aimed at PhD students from wet-lab biomedical and life science graduate programmes who want to improve their practical understanding of statistical analysis. No advanced statistical or R programming experience is required, although familiarity with any coding language (Matlb, Python, Julia, R), and knowledge of common statistical tests will very be helpful. Students who are planning experiments or already have a dataset are highly encouraged to bring a small example from their own research in long-format form (more info will be provided closer in time). During group discussions and practical sessions, students will have opportunities to consider which statistical approach is appropriate for their data, run analyses in R using Google Colab, interpret the output, and receive feedback from peers and instructors.
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
Lectures, group discussions, coding exercises
Course director
Hajime Hirase, Professor, Center for Translational Neuromedicine, hirase@sund.ku.dk
Antonis Asiminas, Assistant Professor, Center for Translational Neuromedicine, a.asiminas@sund.ku.dk
Teachers
Antonis Asiminas, Assistant Professor, Center for Translational Neuromedicine
Dates
10 - 12 May 2027
Course location
7.15.122 (Mærsk Tower)
Registration
Please register before 9 April 2027
Expected frequency
The course is expected to run again in autumn 2027
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.