Related Experiment Video
Updated: Apr 5, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
How to account for uncertainty due to measurement errors in an uncertainty analysis using Monte Carlo simulation
1elhofer@t-online.de
Abstract:
Two kinds of error are considered, namely Berkson and classical measurement error. The true values of the measurands will never be known. Possibly true sets of values are generated by the Monte Carlo simulation of the uncertainty analysis. This is straightforward for Berkson errors but requires the modeling of statistical dependence between measured values and errors in the classical case. A method is presented that enables this dependence modeling as part of the uncertainty analysis. Practical examples demonstrate the applicability of the method. Two "quick fixes" are also discussed together with their shortcomings. The uncertainty analysis of the application of a small computer model from the area of dose reconstruction illustrates, by example, the effect both kinds of error can have on model results like individual dose values and mean value and standard deviation of the population dose distribution.
Related Concept Videos
Uncertainty in Measurement: Accuracy and Precision
Uncertainty in Measurement: Reading Instruments
Random and Systematic Errors
Uncertainty: Overview
Propagation of Uncertainty from Random Error
Propagation of Uncertainty from Systematic Error

