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.
CONTENT
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.
