Entry requirements

120 credits with basic statistics and 60 credits in physics.

Learning outcomes

When the course is completed the student should be able to

• account for the difference between Bayesian and frequentistic statistics
• compare different data,judge the degree of compatibility and correctly treat uncertainties
• carry out function minimisation, both analytically and numerically
• establish confidence intervals
• estimate parameters using established methods
• perform hypothesis testing and relate the result to probability
• utilize common software tools, including Monte Carlo generators, for statistical analysis
• perform an unfolding of a function from data

Content

Practical skills with statistical methods that are used in physics. Bayesian vs. frequentistic statistics, Uncertainties, Probability distributions, Expectation value and variance. Parameter estimation: Method of Moments, Maximum-Likelihood, Least Squares. Hypothesis testing: Chi Square, Signal vs. Background, Kolmogorov-Smirnov test. Function minimisation with constraints. Basic orientation on common software tools, numerical minimizing procedures, simple Monte Carlo generators and unfolding of functions from data.

Instruction

Online lectures, IRL lectures with focus on problem solving. Workshops.

Assessment

Hand-in exercises with oral presentations at the workshop. Active participation at lectures and workshops.