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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: Overview00:59

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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 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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Uncertainty: Confidence Intervals00:54

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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...
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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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Propagation of Uncertainty from Random Error00:59

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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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Updated: Apr 1, 2026

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

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False precision, surprise and improved uncertainty assessment.

Wendy S Parker1, James S Risbey2

  • 1Department of Philosophy, Durham University, Durham, UK wendy.parker@durham.ac.uk.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|October 14, 2015
PubMed
Summary
This summary is machine-generated.

Uncertainty reports require faithfulness and completeness. Avoiding a one-size-fits-all approach and accounting for surprise risks are crucial for accurate uncertainty assessment and reporting.

Keywords:
climate changemodelsprobabilitysurpriseuncertaintyunknown unknowns

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Area of Science:

  • Decision Analysis
  • Risk Management
  • Scientific Communication

Background:

  • Uncertainty reports are vital for conveying an agent's confidence in specific matters.
  • Current uncertainty assessment practices may not fully meet essential reporting requirements.
  • The risk of unforeseen events, or surprises, poses a significant challenge to accurate uncertainty reporting.

Purpose of the Study:

  • To define the fundamental requirements for effective uncertainty reports.
  • To identify common pitfalls in uncertainty assessment that undermine report quality.
  • To propose strategies for improving the faithfulness and completeness of uncertainty reports.

Main Methods:

  • Conceptual analysis of uncertainty reporting standards.
  • Identification and discussion of common biases and errors in uncertainty assessment.
  • Development of recommendations for scientists and commissioning bodies.

Main Results:

  • Two key requirements for uncertainty reports were identified: faithfulness and completeness.
  • Two major pitfalls were highlighted: a uniform approach to uncertainty representation and failure to consider surprise risks.
  • Strategies to mitigate these pitfalls and enhance uncertainty reporting were outlined.

Conclusions:

  • Striving for faithfulness and completeness in uncertainty reports is essential for reliable decision-making.
  • Adopting tailored approaches to uncertainty and proactively considering surprise risks can improve assessment accuracy.
  • Implementing proposed strategies can lead to more robust and trustworthy uncertainty communication.