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Related Concept Videos

Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Uncertainty: Overview00:59

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.
Uncertainty in Measurement: Accuracy and Precision03:37

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: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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 't,' or...
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
Propagation of Uncertainty from Random Error00:59

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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Related Experiment Video

Updated: Jul 15, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

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

Published on: September 7, 2019

Uncertainty from sampling in acceptance sampling.

Analytical Methods Committee Amctb No

    Analytical Methods : Advancing Methods and Applications
    |July 14, 2026
    PubMed
    Summary

    Measurement Uncertainty (MU) should account for Uncertainty from Sampling (UfS). This brief explains incorporating MU and UfS into Acceptance Sampling (AS) for trade compliance decisions.

    Area of Science:

    • Metrology
    • Quality Control
    • Industrial Standards

    Background:

    • Measurement Uncertainty (MU) is integral to measurement procedures.
    • Uncertainty from Sampling (UfS) is a key component of overall MU.
    • Acceptance Sampling (AS) is a trade compliance method predating MU.

    Purpose of the Study:

    • To integrate MU and UfS into Acceptance Sampling (AS) decision-making.
    • To provide a framework for enhanced compliance assessment in trade.

    Main Methods:

    • Conceptual explanation of MU and UfS.
    • Application of MU and UfS principles within the AS framework.
    • Technical brief outlining integration strategies.

    Main Results:

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    Last Updated: Jul 15, 2026

    Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
    10:22

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

    Published on: September 7, 2019

    Sampling Soils in a Heterogeneous Research Plot
    07:11

    Sampling Soils in a Heterogeneous Research Plot

    Published on: January 7, 2019

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    • Demonstrates the feasibility of incorporating UfS into AS.
    • Provides a method to enhance the reliability of trade compliance decisions.

    Conclusions:

    • Acceptance Sampling can be enhanced by incorporating Measurement Uncertainty.
    • Including Uncertainty from Sampling improves the scientific rigor of compliance decisions in trade.