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Related Concept Videos

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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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

Uncertainty in Measurement: Reading Instruments

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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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Contaminants and Errors01:16

Contaminants and Errors

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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...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

884
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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Random and Systematic Errors01:20

Random and Systematic Errors

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Related Experiment Video

Updated: Sep 12, 2025

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry
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Measurement uncertainty for practical use - applied in hematology.

Wytze P Oosterhuis1, Abdurrahman Coskun2, Sverre Sandberg3

  • 1EFLM Committee: Practical Guide to Implement Measurement Uncertainty, Milan, Italy; Reinier Haga Medical Diagnostic Center, Delft, the Netherlands.

Clinica Chimica Acta; International Journal of Clinical Chemistry
|August 10, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a practical approach to calculating measurement uncertainty (MU) for routine hematological parameters in clinical laboratories. The findings show that MU can be effectively determined and improved within a laboratory conglomerate, ensuring reliable diagnostic testing.

Keywords:
Measurement uncertaintyQuality controlReproducibility

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Area of Science:

  • Clinical Laboratory Science
  • Medical Diagnostics
  • Quality Management in Healthcare

Background:

  • Accreditation standards mandate monitoring and reporting of measurement uncertainty (MU) in clinical laboratories.
  • Current guidelines for MU calculations can be complex, necessitating pragmatic approaches.
  • Internal quality control (IQC) data offers a viable basis for MU estimation.

Purpose of the Study:

  • To evaluate an alternative, utilitarian approach for calculating MU in a multi-laboratory setting.
  • To assess MU for routine hematological parameters using IQC data.
  • To identify and address factors contributing to result variability across different measurement systems.

Main Methods:

  • Applied a pragmatic MU calculation method utilizing internal quality control (IQC) results.
  • Analyzed routine hematological parameters across eleven measurement systems in seven laboratories.
  • Employed graphical and variance component analysis to pinpoint sources of divergence.
  • Compared calculated MU against established limits based on biological variation (MU-APS).

Main Results:

  • Measurement uncertainty (MU) for most hematological tests fell within the MU-APS limits.
  • Cellular counting parameters, excluding indices, met biological variation specifications.
  • A correction procedure successfully improved MU for a deviating measurement system.
  • Factors causing divergent results within the laboratory conglomerate were identified and mitigated.

Conclusions:

  • A pragmatic MU calculation approach is feasible for hematological tests in a laboratory conglomerate.
  • Internal quality control data provides a reliable basis for MU estimation in routine hematology.
  • Identified sources of variation can be addressed to improve measurement system performance and reliability.