205067 The Stats Club (S) (WiSe 2022/2023)

This colloquium is designed as a forum for discussing applied biostatistical problems. It offers an on-demand, goal-orientated, hands-on approach to experimental design and statistical analysis of behavioural, ecological and evolutionary data from the daily scientific work of the participants. Any questions arising before (preferred) or after experiments are carried out can be put up for discussion and will be disseminated among participants prior to meetings as a one-page pdf concisely describing the problem. This will provide the background for a joint discussion of the issue, fuelling insight by the collective statistical expertise of all participants.
Your questions may include: What exactly is it that I want to find out? What kind of data should I collect and exactly how? Which specific control(s) do I need? Will my statistical power be sufficient to allow well-supported conclusions? Which is an appropriate statistical model for my type of data? Which error distribution reflects best the underlying stochastic processes? Are there any dependencies in my data and how can I control 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 of my stats software and which conclusions are supported by it (and which are not)?

Requirements for participation, required level

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.

Bibliography

1. Ruxton, G.D. & Colegrave, N. (2016): Experimental Design for the Life Sciences (Fourth Edition). Oxford University Press, Oxford.
2. Beckerman, A.P., Childs D.Z. & Petchey, O.L. (2017): Getting Started with R: An Introduction for Biologists (Second Edition). Oxford University Press, Oxford.
3. Crawley, M.J. (2014): Statistics: An Introduction Using R (Second Edition). John Wiley, New York
(see also http://www.imperial.ac.uk/bio/research/crawley/statistics).