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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

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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.
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Uncertainty: Confidence Intervals00:54

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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...
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Propagation of Uncertainty from Systematic Error01:10

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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...
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Propagation of Uncertainty from Random Error00:59

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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...
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Estimation of the Physical Quantities01:05

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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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. 
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Updated: Nov 11, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations.

D Groen1,2, H Arabnejad1, V Jancauskas3

  • 1Department of Computer Science, Brunel University London, London, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|March 29, 2021
PubMed
Summary
This summary is machine-generated.

The VECMA toolkit (VECMAtk) enhances computational science by providing reusable procedures for verification, validation (V&V), sensitivity analysis (SA), and uncertainty quantification (UQ). This updated version offers improved functionality and performance across diverse scientific domains.

Keywords:
multiscale simulationsuncertainty quantificationvalidationverification

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Area of Science:

  • Computational Science
  • Software Engineering

Background:

  • Single and multiscale simulations require robust methods for verification, validation (V&V), sensitivity analysis (SA), and uncertainty quantification (UQ).
  • Existing software environments may lack integrated and reusable procedures for these critical computational science tasks.

Purpose of the Study:

  • To present functional and performance improvements to the VECMA toolkit (VECMAtk).
  • To introduce new components and application examples for VECMAtk.
  • To demonstrate implemented patterns for UQ/SA and V&V, including a detailed COVID-19 modeling example.

Main Methods:

  • Development of a flexible software environment (VECMAtk) for single and multiscale simulations.
  • Integration of directly applicable and reusable procedures for V&V, SA, and UQ.
  • Testing and application of VECMAtk across seven diverse domains, including conflict modeling and environmental sciences.

Main Results:

  • VECMAtk enables users to verify applications and validate simulation outputs against benchmark data.
  • The toolkit supports running simulations on platforms ranging from desktops to multi-petascale computers.
  • New components and improved performance enhance the usability and scope of VECMAtk for complex modeling tasks.

Conclusions:

  • VECMAtk provides a comprehensive solution for enhancing reliability and reproducibility in computational science.
  • The toolkit facilitates the implementation of V&V, SA, and UQ, crucial for scientific rigor.
  • The continuous development of VECMAtk supports its application in diverse fields, promoting robust scientific discovery.