Assessment of uncertainty in parameter evaluation and prediction

Like in all experimental science, chemical data is affected by the limited precision of the measurement process. Quality control and traceability of experimental data require suitable approaches to express properly the degree of uncertainty. Noise and bias are nuisance effects reducing the information extractable from experimental data. However, because of the complexity of the numerical data evaluation in many chemical fields, often mean values from data analysis, e.g. multi-parametric curve fitting, are reported only. Relevant information on the interpretation limits, e.g. standard deviations or confidence limits, are either omitted or estimated. Modern techniques for handling of uncertainty in both parameter evaluation and prediction are strongly based on the calculation power of computers. Advantageously, computer-intensive methods like Monte Carlo resampling and Latin Hypercube sampling do not require sophisticated and often unavailable mathematical treatment. The statistical concepts are introduced. Applications of some computer-intensive statistical techniques to chemical problems are demonstrated.