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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.

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...

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...

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...

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...

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Related Experiment Video

Updated: Dec 30, 2025

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 Analysis of Consequence Management Data Products.

Lainy D Cochran¹, Aubrey C Eckert, Brian Hunt

¹Sandia National Laboratories, Albuquerque, NM.

|January 28, 2020

Summary

This summary is machine-generated.

This study introduces a probabilistic framework to quantify uncertainty in radiological data products. This allows for customized protective action recommendations based on acceptable uncertainty levels.

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Last Updated: Dec 30, 2025

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

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Radiological assessment
Environmental monitoring
Risk analysis

Background:

US Department of Energy Consequence Management Program develops data products for radiological emergencies.
Federal Radiological Monitoring and Assessment Center (FRMAC) relies on these data products.
Characterizing uncertainty in these products is crucial for effective decision-making.

Purpose of the Study:

To present a probabilistic framework for quantifying uncertainty in FRMAC data products.
To overview probability distributions and statistical methods for uncertainty propagation.
To illustrate the implications of uncertainty analysis for protective action decisions.

Main Methods:

Development of a probabilistic framework for uncertainty characterization.
Application of statistical methods to propagate and quantify uncertainty in input parameters.
Analysis of uncertainty for various study scenarios.

Main Results:

Quantification of overall uncertainty in derived response levels used as contours on data products.
Demonstration of how uncertainty analysis results can be incorporated into data products.
Presentation of customized data product contours based on acceptable uncertainty levels.

Conclusions:

Uncertainty analysis results can inform protective action decisions.
Data product contours can be adjusted to reflect acceptable levels of uncertainty.
Feedback is sought from decision-makers and the radiological emergency response community on presenting and utilizing uncertainty information.