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A5: Introduction to Statistics and Data Analysis

The module introduces important concepts and approaches in descriptive and inferential statistics as they are relevant in drug discovery & development as well as therapeutic use. The overall aim of the module is to understand the theoretical concepts and its underlying assumptions of the different statistical approaches used in pharmacometrics.

Topics include point and set estimation, hypothesis testing, non-linear regression and the non-linear mixed effects approach (establishing the link to the A3 module Introduction to population analysis, including approximation methods (Laplace, FO, FOCE, MCMC), both in the Frequentists as well as the Bayesian context.

Frequency: Every other year in September/October.

Language: English

Module in 2024: 23 - 27 Sep, University of Potsdam/Germany. The schedule below is generic. For each PhD student year, the specific schedule will be sent via email to the participants.

Illustrative schedule

8:45 – 10:45Introduction to statistics and key concepts in probabilityEstimation: point estimators, confidence intervalsNon-linear regression, two-stage approachNon-linear mixed effect (NLME) approachBayesian statistics
30 minCoffee break
11:15 - 13:00Introduction to R and first hands-on with RHands-on with RHands-on with RHands-on with RHands-on with R
60 minLunch break
14:00 - 16:00Descriptive and inferential statisticsHypothesis testingGuest talk on statistics in industryBootstrap, data partitioningMonte Carlo approaches, summary, feedback & closing
15 minCoffee break
16:15 - 17:45Hands-on with RHands-on with RSocial eveningHands-on with R
EveningSocial Event (most likely on Wednesday)


  • Prof. Wilhelm Huisinga; theoretical lectures
  • Dr. Niklas Hartung; hands-on exercises
  • External contributions by our faculty members

Hard- and software

  • Please bring your own laptop.
  • The practical exercises will be in R. Please download the latest version here. You might want to download as well RStudio, which gives you a powerful GUI to R.


  • Detailed references will be provided during the course.
  • Kyle Siegrist, Random (The Virtual Laboratory in Probability and Statistics), online resource @ University of Alabama in Huntsville. A very nice useful online resource with many applets.
  • Larry Wasserman, All of Statistics: A Concise Course in Statistical Inference, Springer, 2004 (2nd corrected printing)