Statistics are essential for scientific research. Good statistical knowledge helps to set up experiments and observational studies effectively and to answer relevant research questions. Through the availability of statistical software, researchers are able to analyse complex designs and data independently.
Date: Nov 10 – 18, 2014
Target audience: Researchers (Higher Education or University), working at businesses, government, research institutes and teachers in higher (agricultural) education.
Program: The course offers you the general principles of experimental design, data analysis and modelling. Different statistical methods are illustrated using examples from the life sciences (plant, animal, food and environment). Experimental design, from simple to complex, analysis of variance and generalized linear models are explained and practised. After this course the researcher can apply these techniques in his or her own research. The main focus of this course is the practical application of statistical methods and modelling on examples in the life sciences. In each part of research it’s essential to make the right choices and therefore the emphasis is on learning to understand which statistical method is needed given the experiment or research question. The mathematical background is explained when needed. Each course day consists of a part with lectures and a hands-on computer practical with GenStat (16-the edition). The book An Introduction to Statistical Methods and Data Analysis by R. Lyman Ott and Michael T. Longnecker is handed out and will be used for training and self-study.