Information from experimental data. Description of data. Descriptive and inferential statistics.Probability and its interpretation. Randon variables. Data description, probability density function, cumulative distribution function, covariance matrix, error propagation. Important distribution functions, law of large numbers, central limit theorem. Monte Carlo simulations. Point estimation of parameters. Estimation of mean, variance and covariance. Extended maximun likelihood and least squares methods. Interval estimation of parameters. Confidence intervals. Point and interval Bayesian estimators. Hypotheses testing, test statistics, Neyman-Pearson lemma, Goodness of fit tests. Pearson's χ^2 test and Kolmogorov-Smirnov test. Fisher discriminant, neural networks, boosted decision tree, and random forest. Theory-experiment comparison. Resolution function.