Describing the data, theoretical distributions, errors, estimations – likelihood function, basic estimators, maximum likelihood, the method of moments, least squares method, chi-square distribution, Probability and confidence – basic terms, mathematical probability, Bayesian statistics, confidence level, binomial confidence intervals, Poisson confidence intervals. Taking decisions – hypothesis testing (significance, power, Neyman Pearson test), interpreting experiments, the null hypothesis, binomial probabilities, goodness of fit, ?2 test, the run test, the Kolmogorov test. Two sample problem, matched and correlated samples. Ranking methods – nonparametric methods, Mann-Whitney test, matched pairs, Wilcoxon`s matched pairs signed rank test, measures of agreement – Spearmen`s correlation coefficient, concordance. Usage of computers for data processing and software features.

Type of methodology: Combination of lecture and hands-on

Participants receive the certificate of attendance: Yes

Paid training activity for participants: Yes, for all

Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge