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“To consult the statistician after an experiment is finished is often merely to ask them to
conduct a post mortem examination. They can perhaps say what the experiment died of.” ‐ Sir Ronald Aylmer Fisher, the father of modern statistics.
Statistical experimental design, accompanied by the appropriate statistical analyses, plays a crucial role in producing valid and precise inferences, and avoiding ‘design disasters’ in empirical science. I will quickly refresh some of the basic elements of experimental design in general, and discuss some key issues and examples that arise
in the areas of genetics/genomics and high throughput data.
First presented at the 2014 Winter School in Mathematical and Computational Biology http://bioinformatics.org.au/ws14/program/