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Related Concept Videos

Uncertainty: Overview00:59

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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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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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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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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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Uncertainty in measurements can be avoided by reporting the results of a calculation with the correct number of significant figures. This can be determined by the following rules for rounding numbers:
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Uncertainty Calculation for Spectral-Responsivity Measurements.

John H Lehman1, C M Wang1, Marla L Dowell1

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

Journal of Research of the National Institute of Standards and Technology
|August 10, 2016
PubMed
Summary
This summary is machine-generated.

This study presents a method for measuring optical-fiber power meter responsivity and calculating calibration uncertainty. It reconciles monochromator and laser-based measurements, offering a reliable approach for accurate optical power calibration.

Keywords:
lasermonochromatoroptical fiberoptical powerspectral responsivityuncertainty calculation

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Area of Science:

  • Metrology and Measurement Science
  • Optical Engineering
  • Instrumentation

Background:

  • Accurate measurement of optical-fiber power meter spectral responsivity is crucial for reliable optical power calibration.
  • Existing measurement procedures may yield discrepancies between different setups, necessitating reconciliation.
  • Quantifying calibration uncertainty is essential for establishing confidence in measurement results.

Purpose of the Study:

  • To detail a procedure for measuring the absolute spectral responsivity of optical-fiber power meters.
  • To compute and analyze the calibration uncertainty associated with these measurements.
  • To reconcile measurement data obtained from distinct systems (monochromator vs. laser sources).

Main Methods:

  • Developed a measurement procedure for absolute spectral responsivity using optical-fiber power meters.
  • Employed both monochromator-based systems and laser sources coupled with optical fiber.
  • Applied uncertainty calculation methods from the Guide to the Expression of Uncertainty in Measurement and Monte Carlo simulations.

Main Results:

  • Successfully reconciled measurement results from monochromator and laser-based systems.
  • Derived and compared relative expanded uncertainties using established metrology guidelines.
  • Demonstrated the procedure and uncertainty calculations with a practical example.

Conclusions:

  • The proposed procedure provides a robust method for accurate spectral responsivity measurement of optical-fiber power meters.
  • The reconciliation of different measurement systems enhances the reliability of calibration.
  • The detailed uncertainty analysis ensures traceability and comparability of measurement results.