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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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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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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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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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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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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 19, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Modeling Uncertainty for the Double Standard Model Using a Fuzzy Inference System.

Noelia Torres1, Leonardo Trujillo1, Yazmin Maldonado1

  • 1Departamento de Ingenieria Electrica y Electronica, Instituto Tecnologico de Tijuana, Tijuana, Mexico.

Frontiers in Robotics and AI
|January 27, 2021
PubMed
Summary
This summary is machine-generated.

This study addresses ambulance location problems by incorporating travel time uncertainty. A fuzzy inference system (FIS) approach improves demand coverage compared to traditional methods, enhancing emergency response planning.

Keywords:
ambulancesbasesdouble standard modelemergency medical servicesfuzzy inference systemtriangular fuzzy set

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Area of Science:

  • Operations Research
  • Public Health Management
  • Computer Science

Background:

  • The ambulance location problem seeks optimal placement for maximum demand coverage.
  • The double standard model (DSM) is a common approach, but often overlooks travel time variability.
  • Uncertainty in travel times can significantly impact emergency service effectiveness.

Purpose of the Study:

  • To investigate the impact of travel time uncertainty on the ambulance location problem.
  • To develop and evaluate a fuzzy inference system (FIS) for handling this uncertainty.
  • To compare the FIS approach with traditional DSM methods.

Main Methods:

  • Utilized the double standard model (DSM) framework.
  • Introduced uncertainty in travel times using triangular fuzzy sets.
  • Developed a novel fuzzy inference system (FIS) with a rule base derived from problem characteristics.
  • Solved linear programs to determine optimal ambulance placement under different uncertainty scenarios.

Main Results:

  • Considering travel time uncertainty significantly alters optimal ambulance location solutions.
  • The fuzzy inference system (FIS) approach demonstrated superior performance in maximizing demand coverage.
  • Solutions incorporating uncertainty provided more robust and reliable outcomes for emergency services.

Conclusions:

  • Uncertainty in ambulance travel times is a critical factor in location planning.
  • The proposed fuzzy inference system (FIS) offers a reliable and effective strategy for the ambulance location problem.
  • This approach can significantly improve decision-making for emergency medical services by accounting for real-world travel variability.