Target group 
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:
 Study Track in Particle Physics and Cosmology
The course is available to students from other degree programmes. 
Timing 
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. 
Learning outcomes 
After the course, the student will...
 learn to know the basics of statistics and statistical distribution as well as being able to apply the correct distribution.
 understand hypotheses testing and different methods for hypotheses testing as well as the strengths and weaknesses of the methods.
 understand parameter estimation based on maximum likelihood and least squares methods as well as the strengths and weaknesses of the methods.
 being able to apply methods of hypothesis testing and parameter estimation as well as make the correct statistical interpretation.
 being familiar with confidence intervals and unfolding.

Completion methods 
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). 
Prerequisites 
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. 
Recommended optional studies 
 PAP331 Computing Methods in High Energy Physics
 MATR322 Scientific Computing III
 MATR323 Basics of Monte Carlo Simulations

Contents 
 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, goodnessoffit 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.

Study materials and literature 
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). 
Activities and teaching methods in support of learning 
Weekly lectures and exercises (individual work). Final exams. Total hours 135. 
Assessment practices and criteria 
Final grade based on best two out of three with equal 50 % weight: exercises, final home exam and final written exam (optional). 