Statistical Methods in Particle Physics

winter term 2022/2023
Lecturer: Rainer Stamen
Link to LSF
19 participants

Niels Bohr supposedly said if quantum mechanics did not make you dizzy then you did not really understand it. I think the same can be said about statistical inference. 

Robert D. Cousins, Why isn't every physicist a Bayesian

Learning goals and required knowledge

This course introduces basic concepts and statistical methods used in particle physics. It is a natural follow-up to the PEP4 lecture for bachelor students. Master students can attend this lecture in parallel or after the Particle Physics course.

The concepts and techniques discussed here are also applied in fields different from particle physics. 

Learning goals

  • Get to know and apply the toolobx of statistical methods used in particle physics.
  • Understand error bars and confidence limits as reported in publications.
  • Solid understanding of maximum likelihood and least squares fits.
  • From measurement to message: Which conclusion can you draw from your data (and which not)?
  • Learn to apply machine learning methods.

Required knowledge

  • Basic understanding of experimental particle physics (as taught in the bachelor's course)
  • Basic knowledge of Python.

Practical Information


  • Lecture will be in presence
  • Thursdays: 16:15 - 17:45, KIP HS-2 (INF 227)
  • First Lecture: 20. 10. 2022


  • Tutorials will be in presence in the KIP CIP Pool
  • Please register for one of the two groups
  • Group 1 (Mo: 16:00 - 18:00)
  • Group 2 (Tu: 16:00 - 18:00)
  •  First Tutorial: 24/25. 10. 2022
  • Used software: Python + jupyter notebooks
  • The KIP Jupyter server can be used to work on the exercises from any browser
  • You can also install Jupyter notebooks on your own computer


  • You need to register for one of the two groups.

Exercise Sheets

  • will become available online every week after the lecture.
  • can be downloaded from ths web page.
  • to be handed in by Thursday 12:00 of the following week (electronically through the Übungsgruppenverwaltung).
  • will be handed out as jupyter notebook.
  • should be handed in as jupyter notebook as well or as pdf file (scan of handwritten solution).
  • At least 60% of the points are required to qualify for the exam.


  • Date: to be defined
  • Format: in presence (most likely)
  • will be graded. 

ECTS points

  • Lecture and Tutorials: 4 ECTS points


  • A large fraction (>50%) of the problems consist of the implementation of statistical methods in Python in the framework of jupyter noebooks.
  • A basic level of knopwledge in Python is needed. Advanced modules will be used but special knowledge is not needed prior to the lecture.
  • There are several good tutorials and courses on the internet which you can use to acquire a basic programming level.
  • The tutorials start only in the second week and the first problem sheet will be handed out on Thursday of the first week. So you might consider the first few days to go through some of these Python courses.


  • G. Cowan: Statistical Data Analysis
  • Behnke, Kroeninger, Schott, Schoerner-Sardenius: Data Analysis in High Energy Physics: A Practical Guide to Statistical Methods
  • Claude A. Pruneau: Data Analysis for Physics Scientists
  • L. Lista: Statistical Methods for Data Analysis in Particle Physics
  • R. Barlow: Statistics: A guide to the Use of Statistical Methods in the Physical Sciences
  • Bohm, Zech: Introduction to Statistics and Data Analysis for Physicists
  • Blobel, Lohrmann: Statistische Methoden der Datenanalyse
  • F. James: Statistical Methods in Experimental physics
  • W. Metzger: Statstical Methods in Data Analysis

Practice groups

  • Group 1 (Martin Völkl / Klaus Reygers)
    19 participants
    INF 227 1.401 CIP Pool, Mon 16:00 - 18:00
Statistical Methods in Particle Physics
winter term 2022/2023
Link zum LSF
19 participants