On completion of the course, the student should be able to
describe statistical models
choose methods to evaluate different types of empirical data
use the most important and most common statistical methods
present the philosophy and the arguments behind experimental design and hypothesis testing.
The course starts from the students' knowledge about basic statistical concepts such as measures of central tendency and variation and hypothesis testing. The aim is to give a good overview over the statistical toolbox that is used for the analysis of empirical data, especially within biology. The course covers analysis of experimental data (ANOVA, ANCOVA, including block experiments, repeated measurement, nested and factorial experiments) but also observational data (regression including methods to choose predictors and evaluate models generalised linear models (GLIM) with logistic and Poisson distribution). Introduction to power analysis, multivariate analysis, resampling and permutation techniques. A short introduction to the program R is also offered.
Lectures, literature discussions and individual computer exercises (analysis of example data).
A passing grade requires both attendance at all parts and passed presentations of computer exercises.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.