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Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

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.

Uncertainty in Measurement: Reading Instruments

Uncertainty in Measurement: Reading Instruments

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...

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...

Testing a Claim about Standard Deviation

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...

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...

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Nanoparticle Tracking Analysis of Gold Nanoparticles in Aqueous Media through an Inter-Laboratory Comparison

Nanoparticle Tracking Analysis of Gold Nanoparticles in Aqueous Media through an Inter-Laboratory Comparison

Published on: October 20, 2020

Transfer Standard Uncertainty Can Cause Inconclusive Inter-Laboratory Comparisons.

John Wright¹, Blaza Toman¹, Bodo Mickan²

¹National Institute of Standards and Technology (NIST).

Metrologia

|January 17, 2017

Summary

Inter-laboratory comparisons face challenges with unstable transfer standards. New criteria improve assessment of measurement comparison results, ensuring accurate evaluation of laboratory performance and uncertainty claims.

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Area of Science:

Metrology
Measurement Science
Analytical Chemistry

Background:

Inter-laboratory comparisons are crucial for validating measurement uncertainty and identifying biases.
Instability in transfer standards can compromise the reliability of comparison results.
Existing criteria may fail when transfer standard uncertainty is high relative to participant uncertainty.

Purpose of the Study:

To address the limitations of current methods for assessing inter-laboratory comparisons with unstable transfer standards.
To propose and validate new criteria for evaluating comparison outcomes.
To improve the accuracy of assessing whether laboratories operate within their claimed uncertainties.

Main Methods:

Demonstrated issues with the standardized degree of equivalence using specific comparison results.
Proposed alternative criteria for classifying comparison results (passing, failing, inconclusive).
Investigated the performance of standardized degree of equivalence and alternative measures across varying uncertainty ratios.

Main Results:

The standardized degree of equivalence criterion can be unreliable when transfer standard uncertainty is significant.
Proposed alternative criteria effectively distinguished between passing, failing, and inconclusive results.
The study provides a more robust framework for evaluating inter-laboratory comparisons.

Conclusions:

Current methods for inter-laboratory comparisons may yield inconclusive results due to transfer standard instability.
The proposed alternative criteria offer a more reliable assessment of laboratory performance in metrology.
These advancements enhance the integrity of the global measurement system by improving uncertainty analysis validation.