The module offers a comprehensive introduction to applied statistics and its fundamentals. In the area of descriptive statistics, position and dispersion measures, empirical quantiles, histograms, the raw and smoothed empirical distribution function, empirical correlation and equalisation (regression) are covered.

In the theoretical foundations, participants learn about probability models, set theory and combinatorics in order to calculate probabilities in complex scenarios. Random variables, probability and density functions, distribution functions and quantile functions are also discussed. The importance of expected value, variance, covariance and correlation coefficient is taught. Participants also learn about the law of large numbers and the central limit theorem.

In the field of inductive statistics, a distinction is made between moment and maximum likelihood methods in estimation theory. The practical module also teaches test theory and methods.