Kaisa_2012_3_photo by Veikko Somerpuro

18.12.2019 at 08:00 - 7.1.2020 at 23:59



Master’s Programme in Particle Physics and Astrophysical Sciences is responsible for the course.

Module where the course belongs to:

  • PAP300 Advanced Studies in Particle Physics and Astrophysical Sciences
    Optional for:
    1. Study Track in Particle Physics and Cosmology

The course is available to students from other degree programmes.

Able to use some statistical library or tool (Matlab, Octave, ROOT, etc...) for numerical calculation or simulation.

Programming and usage of statistical libraries or tools are not taught during this course.

  • PAP331 Computing Methods in High Energy Physics
  • MATR322 Scientific Computing III
  • MATR323 Basics of Monte Carlo Simulations

After the course, the student will...

  1. learn to know the basics of statistics and statistical distribution as well as being able to apply the correct distribution.
  2. understand hypotheses testing and different methods for hypotheses testing as well as the strengths and weaknesses of the methods.
  3. understand parameter estimation based on maximum likelihood and least squares methods as well as the strengths and weaknesses of the methods.
  4. being able to apply methods of hypothesis testing and parameter estimation as well as make the correct statistical interpretation.
  5. being familiar with confidence intervals and unfolding.

Can be taken at any stage of master's or doctoral studies.

The course is offered every year in the autumn term, in I and II period.

  • Fundamental concepts: experimental errors and their correct interpretation, frequentist & Bayesian interpretation of probability, the most common statistical distributions and their applications.
  • Monte Carlo methods: basics of Monte Carlo methods and generation of an arbitrary distribution.
  • Hypothesis testing: the concept of hypothesis testing, a test statistic, discriminant multivariate analysis, goodness-of-fit tests and ANOVA.
  • Parameter & error estimation: the concept of parameter estimation, an estimator, the maximum likelihood method and the method of least squares.
  • Confidence intervals & Unfolding: basics about setting confidence intervals and making unfolding.

Main material:

Lecture notes;

G. Cowan: Statistical Data Analysis (Oxford University Press 1998.

Supplymentary reading:

Particle Data Group Reviews on Probability, Statistics & Monte Carlo techniques (available at pdg.lbl.gov).

Weekly lectures and exercises (individual work). Final exams. Total hours 135.

Final grade based on best two out of three with equal 50 % weight: exercises, final home exam and final written exam (optional).

Course completion based on sufficient points in two out of three methods: weekly exercises based on lectures, final home exam and final written exam (optional).