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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Random Error

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...

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Related Experiment Video

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Uncertainty reduction in environmental data with conflicting information.

Alberto Fernández¹, Robert Rallo, Francesc Giralt

¹Departament d'Enginyeria Química, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalunya, Spain.

Environmental Science & Technology

|August 14, 2009

Summary

The Dempster-Shafer theory effectively manages uncertainty in chemical ecotoxicology data, reducing estimation errors by up to 60%. This approach aids regulatory decisions with limited or conflicting information.

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Area of Science:

Environmental toxicology
Computational toxicology
Decision science

Background:

Regulatory ecotoxicology relies on extensive, costly experimental data, potentially involving animal testing.
Decision-making in chemical risk assessment often faces limited or contradictory information sources.
Managing and reducing uncertainty is crucial for robust regulatory assessments.

Purpose of the Study:

To apply the Dempster-Shafer theory of evidence for uncertainty management in chemical persistence assessment.
To integrate and reconcile conflicting data from experimental and in silico sources.
To evaluate the effectiveness of the Dempster-Shafer theory in improving biodegradation rate estimations.

Main Methods:

Application of the Dempster-Shafer theory to combine experimental and in silico biodegradation data.
Quantification and redistribution of conflicting evidence among hypotheses.
Comparative analysis against Bayesian approaches for evidence representation.

Main Results:

Uncertainties in biodegradation rate estimates were reduced by 20-60%.
The Dempster-Shafer theory successfully detected and quantified conflicting evidence.
Proportional redistribution of conflicting evidence across feasible hypotheses was achieved.

Conclusions:

The Dempster-Shafer theory offers a robust framework for managing uncertainty in ecotoxicological data assessment.
This method enhances the reliability of chemical persistence evaluations, especially with limited data.
It provides advantages over traditional methods in handling complex and conflicting evidence for regulatory decision-making.