This seminar is designed as a regular meeting for discussing and dissecting applied biostatistical problems. It offers an on-demand, goal-orientated, hands-on approach to experimental design and statistical treatment of ecological & evolutionary data from the daily scientific work of the participants. Any questions arising before (preferred!) or after experiments are carried out, can be handed in and will be disseminated among participants before meetings take place. This will provide background for a joint discussion of the issue, fuelling insight by the collective statistical expertise of all participants.
Questions may include: What /exactly/ is it that I want to know? What kind of data should I collect and exactly how? Given limited sample sizes, will the statistical power be sufficient to allow well-supported conclusions after my study? Which is an appropriate statistical model for my type of data? Which error distribution reflects best the underlying stochastic processes? Is there dependency in my data and how can I control the risk of pseudoreplication? Which variables should be considered as fixed /versus/ random effects terms in my model? Should interaction terms also be considered and what do they imply? How should I interpret the output from my stats software and what conclusions are supported by it?
Teilnahmevoraussetzungen, notwendige Vorkenntnisse
It is advantageous to have some knowledge of basic terms and the concept of statistical tests and standard statistical methods (e.g. regression, ANOVA). This colloquium is not intended to replace a systematic biostatistics course. Discussions in this seminar may be in German or English.
1. Ruxton, G.D. & Colegrave, N. (2010): Experimental Design for the Life Sciences. Oxford University Press, Oxford.
2. Beckerman, A.P. & Petchey, O.L. (2012): Getting Started with R: An Introduction for Biologists. Oxford University Press, Oxford.
3. Crawley, M.J. (2005): Statistics: An Introduction Using R. John Wiley, New York
(see also http://www.imperial.ac.uk/bio/research/crawley/statistics).
4. The R Project for Statistical Computing @ http://www.r-project.org.