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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: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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

Propagation of Uncertainty from Systematic Error

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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...
450
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

620
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...
620
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.2K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
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Updated: May 22, 2025

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
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Decoding uncertainty for clinical decision-making.

Krasimira Tsaneva-Atanasova1, Giulia Pederzanil2, Marianna Laviola3

  • 1Department of Mathematics and Living Systems Institute, University of Exeter, Exeter, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|March 13, 2025
PubMed
Summary

Uncertainty quantification (UQ) is vital for reliable clinical decisions. Addressing data uncertainties improves medical evaluations, leading to better patient outcomes.

Keywords:
clinical data analysisdiagnostic toolsevidence-based medicinemedical innovationprecision medicineuncertainty quantification

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Area of Science:

  • Healthcare Analytics
  • Medical Informatics
  • Clinical Decision Support

Background:

  • Clinical decision-making relies on data, which inherently contains uncertainties.
  • Existing healthcare data presents challenges and barriers to adopting advanced analytical methods like UQ.
  • Understanding and managing these uncertainties is crucial for accurate medical evaluations.

Purpose of the Study:

  • To examine the role of uncertainty quantification (UQ) in clinical decision-making.
  • To explore challenges in healthcare data and barriers to UQ adoption.
  • To highlight how UQ can enhance the precision and reliability of medical evaluations.

Main Methods:

  • This is an opinion piece, discussing the conceptual framework and importance of UQ.
  • Analysis of challenges in healthcare data and potential barriers to UQ implementation.
  • Review of how UQ methodologies can address uncertainties in diagnostic tools and treatment outcomes.

Main Results:

  • UQ techniques can significantly improve the accuracy and robustness of clinical decisions.
  • Systematic analysis of uncertainties in clinical data (e.g., measurement error) is essential.
  • Effective UQ leads to enhanced patient outcomes and more informed healthcare practices.

Conclusions:

  • Uncertainty quantification is pivotal for reliable clinical decision-making.
  • Overcoming barriers to UQ adoption is necessary for advancing healthcare.
  • Implementing UQ enhances the precision of medical evaluations and patient care.