Table of Contents

Part 1 Modelling the error for directly measured observation: random and systematic errors; basic theoretical concepts. Part 2 Estimation of parameters of probability distributions: confidence intervals; estimation of the variance; verification of the basic assumptions; choice of a probability distribution; estimation of the mean; study of variations in m and sigma using Page's test. Part 3 Comparison of means estimation of components of error: single factor experiments; factorial designs; nested designs. Part 4 Uncertainty in the final result: effect of one error factor; effect of several error factors; sampling involving a finite numbe of objects; characteristics of a measurement method; detection limit. Part 5 Calculations and use of calibration curves: straight-line calibration lines; polynomial regression; nonlinear relationships; systems curves; uncertainty due to calibration; limit of detection.