On completion of the course, 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
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.
Online lectures, IRL lectures with focus on problem solving. Workshops.
Hand-in exercises with oral presentations at the workshop. Active participation at lectures and workshops.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.