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Related Concept Videos

Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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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. 
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Uncertainty in Measurement: Reading Instruments02:46

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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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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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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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Uncertainty in Measurement: Significant Figures03:34

Uncertainty in Measurement: Significant Figures

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All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
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Propagation of Uncertainty from Systematic Error01:10

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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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Updated: Sep 24, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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Measurement uncertainty for practical use.

Abdurrahman Coskun1, Elvar Theodorsson2, Wytze P Oosterhuis3

  • 1EFLM Task and Finish Group on Practical Approach to Measurement Uncertainty, Milan, Italy; School of Medicine, Department of Medical Biochemistry, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey.

Clinica Chimica Acta; International Journal of Clinical Chemistry
|May 5, 2022
PubMed
Summary
This summary is machine-generated.

Clinical laboratories can simplify measurement uncertainty calculations by using a pragmatic approach. This method focuses on influential factors and commutable quality control materials for accurate patient test results.

Keywords:
CommutabilityHarmonizationInternal quality controlMeasurement uncertainty

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Area of Science:

  • Clinical Laboratory Science
  • Measurement Science

Background:

  • Accreditation standards (ISO/IEC 17025:2017, ISO 15189:2012) mandate measurement uncertainty calculations in clinical laboratories.
  • Existing guidelines (CLSI EP29, Nordest 537, ISO 20914:2019) offer methods but are often perceived as too complex for routine application.

Purpose of the Study:

  • To describe a pragmatic and utilitarian approach for calculating measurement uncertainty in clinical laboratories.
  • To outline the advantages and disadvantages of this simplified method.

Main Methods:

  • Focus on incorporating the most influential factors affecting patient test results into uncertainty calculations.
  • Utilize results from analyzing the same internal quality control material (if commutable) or pooled/split patient samples.
  • This approach is suitable for scenarios involving multiple laboratories or diverse measuring systems for the same measurand.

Main Results:

  • The proposed approach aims to enhance the practical application of measurement uncertainty in high-workload clinical settings.
  • It offers a more accessible method compared to complex, guideline-based calculations.
  • The effectiveness and limitations of this utilitarian approach are discussed.

Conclusions:

  • A pragmatic approach to measurement uncertainty calculation is feasible and beneficial for clinical laboratories.
  • This method addresses the challenges posed by high workload and measurand heterogeneity.
  • Accurate calculation of measurement uncertainty is crucial for reliable patient test results.