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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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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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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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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 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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Significant Figures in Calculations00:58

Significant Figures in Calculations

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Uncertainty in measurements can be avoided by reporting the results of a calculation with the correct number of significant figures. This can be determined by the following rules for rounding numbers:
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Communicating scientific uncertainty.

Baruch Fischhoff1, Alex L Davis2

  • 1Departments of Engineering and Public Policy and Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213-3890 baruch@cmu.edu.

Proceedings of the National Academy of Sciences of the United States of America
|September 17, 2014
PubMed
Summary
This summary is machine-generated.

Effective communication of scientific uncertainty is crucial for informed decision-making. This study categorizes decisions and proposes a protocol to clearly convey uncertainty, improving science and its support.

Keywords:
expert elicitationexpert judgmentmental modelsriskscience communication

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Area of Science:

  • Decision Science
  • Risk Communication
  • Scientific Uncertainty

Background:

  • All scientific endeavors involve uncertainty.
  • Ineffective communication of uncertainty can lead to poor decision-making.
  • Understanding decision contexts is key to effective uncertainty communication.

Purpose of the Study:

  • To examine how to characterize, assess, and convey uncertainty for different decision types.
  • To develop a protocol for summarizing scientific uncertainty.
  • To improve decision-making, scientific practice, and support for science.

Main Methods:

  • Categorization of decisions into three types: signal detection, option selection, and option creation.
  • Analysis of uncertainty characterization, assessment, and communication strategies for each decision type.
  • Development of a standardized protocol for uncertainty summarization.

Main Results:

  • Identified three distinct classes of decisions influencing uncertainty communication needs.
  • Proposed a protocol to standardize the communication of scientific uncertainty.
  • The protocol aims for minimal burden on scientists and gradual education for decision-makers.

Conclusions:

  • Effective communication of scientific uncertainty is vital for informed decision-making across various contexts.
  • A standardized protocol can bridge the gap between scientific findings and practical application.
  • The proposed approach supports better decisions, enhances scientific rigor, and strengthens the interface between science and society.