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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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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

Propagation of Uncertainty from Random Error

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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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Random Error01:04

Random Error

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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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Maxwell-Boltzmann Distribution: Problem Solving01:20

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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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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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Related Experiment Video

Updated: Mar 17, 2026

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

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Noise-Parameter Uncertainties: A Monte Carlo Simulation.

J Randa1

  • 1National Institute of Standards and Technology, Boulder, CO 80305.

Journal of Research of the National Institute of Standards and Technology
|July 23, 2016
PubMed
Summary
This summary is machine-generated.

This study quantifies uncertainties in noise-parameter measurements using Monte Carlo simulations. Results show how input variations impact noise parameter accuracy, aiding measurement enhancement.

Keywords:
amplifier noisemeasurement errorsnoisenoise measurementsimulationuncertainty

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Area of Science:

  • Electrical Engineering
  • Metrology

Background:

  • Accurate noise-parameter measurements are crucial for electronic device characterization.
  • Quantifying uncertainties in these measurements is essential for reliable performance evaluation.

Purpose of the Study:

  • To develop and apply a Monte Carlo simulation to assess uncertainties in noise-parameter measurements.
  • To determine the impact of various underlying quantity uncertainties on noise parameter accuracy.

Main Methods:

  • Formulation of a Monte Carlo simulation model.
  • Computation of the dependence of noise parameter uncertainty on underlying quantity uncertainties.
  • Analysis of uncertainties from reflection coefficients, noise source temperature, connector variability, ambient temperature, and output noise measurement.

Main Results:

  • Quantified the impact of individual uncertainty sources on noise-parameter measurements.
  • Presented results for both uncorrelated and correlated uncertainties.
  • Evaluated the effectiveness of using a cold noise source and measuring the 'reverse configuration' for measurement enhancement.

Conclusions:

  • The Monte Carlo simulation effectively models uncertainties in noise-parameter measurements.
  • Identified key sources contributing to measurement uncertainty.
  • Demonstrated potential enhancements for improving the accuracy of noise-parameter measurements.