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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 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...

Uncertainty: Confidence Intervals

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...

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...

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...

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Related Experiment Video

Updated: Dec 25, 2025

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

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

Published on: September 7, 2019

Reference change values based on uncertainty models.

Raúl Rigo-Bonnin¹, Diego Muñoz-Provencio¹, Francesca Canalias²

¹Laboratori Clínic, IDIBELL, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.

Clinical Biochemistry

|April 4, 2020

Summary

This summary is machine-generated.

New uncertainty-based models provide more realistic reference change values by incorporating all variation sources. These models offer a better understanding for disease monitoring in clinical laboratories.

Keywords:

Analytical phase Biological variation Post-analytical phase Pre-analytical phase Reference change values Uncertainty

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Last Updated: Dec 25, 2025

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

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Published on: September 7, 2019

An R-Based Landscape Validation of a Competing Risk Model

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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

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Published on: September 19, 2012

Area of Science:

Clinical Chemistry
Biomedical Engineering
Statistical Modeling

Background:

Reference change values (RCVs) are crucial for interpreting biological measurements.
Existing RCV methods may not account for all sources of variation.
This study introduces novel uncertainty-based models for RCV estimation.

Purpose of the Study:

To develop and validate two new models for estimating reference change values.
To incorporate all potential sources of variation into RCV calculations.
To compare the proposed models with classical RCV approaches.

Main Methods:

Estimating measurement uncertainty for individual biological values.
Calculating the change between consecutive measurements and its associated uncertainty.
Conducting a compatibility study to assess the equivalence of measurements.

Main Results:

Within-subject biological variation was the primary source of uncertainty.
Analytical, pre-analytical, and post-analytical phases also contributed to uncertainty.
The proposed uncertainty-based models yielded higher RCVs compared to classical methods.

Conclusions:

Uncertainty-based RCV models offer a more comprehensive and realistic assessment.
The models account for a wider range of variation sources than traditional methods.
This research encourages clinical laboratories to adopt uncertainty and RCV studies for improved disease monitoring.