The aim of this course is to provide an understanding of the statistical principles underlying experimentation. A proper set-up of an experiment is of utmost importance to be able to draw statistically sound conclusions.
The role of sample size, randomization and the reduction of unwanted noise factors will be highlighted. The way errors propagate will be discussed. The difference between experimental unit and measurement units and consequences for statistical analysis will be discussed.
Examples of basic designs (CRD, RCBD, BIBD), but also more advanced designs will be discussed. Lectures will be interchanged with computer practicals, using R. The final half day will be devoted to discussion of the own experimental designs of the participants.
- Understanding the principles of experimental design: the role of sample size, randomization and reduction of noise factors in the efficient set-up of experiments
- Experimental units vs measurement units, and consequences for statistical analysis
- Examples of commonly used experimental designs, and some more advanced designs
- Basic understanding of propagation of errors (variances)
Former occurrences of this course
19-22 December 2022