Basic concepts of probability theory. Measurement result, distribution function, mean, standard deviation, covariance. Poisson distribution, Gaussian distribution, Student distribution, chi-square distribution, confidence intervals. Parameter estimation. Concept of statistics, estimated parameters. Properties of estimates: unbiasedness, efficiency, consistency. Least squares method. Normal equations and their solution. Estimation of the standard deviation of estimated parameters. Examples for evaluating measurements from different fields of physics. Linear regression. Smoothing curves. Handling nonlinear joints, iteration. Corrections, e.g. dead time correction. Basic metrological concepts. Systematic and statistical error. Consideration of corrections. Concept of measurement uncertainty, estimation methods. Examples of the format for presenting measurement results. Creating graphs. Erroneous measurements. Detection and management of scattered-out points. Laboratory measurement evaluation tasks.