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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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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

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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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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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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Related Experiment Video

Updated: Nov 21, 2025

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
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Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

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Prediction and Learning: Understanding Uncertainty.

Daniel Yon1

  • 1Department of Psychology, Goldsmiths, University of London, London, UK, and Department of Psychological Sciences, Birkbeck, University of London, London, UK.

Current Biology : CB
|January 12, 2021
PubMed
Summary
This summary is machine-generated.

Our brains construct world models to navigate uncertainty, aiding perception and learning. New research uncovers a shared neurocomputational basis for predictive processing across different cognitive functions.

Related Experiment Videos

Last Updated: Nov 21, 2025

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
09:07

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Modeling

Background:

  • Humans constantly build internal models of the external world.
  • These models are crucial for perception, learning, and decision-making under uncertainty.
  • Predictive processing is a key framework for understanding how the brain anticipates future events.

Discussion:

  • This study identifies a core neurocomputational mechanism underpinning predictive processing.
  • Evidence suggests this mechanism is not domain-specific but integrates information across various cognitive functions.

Key Insights:

  • A unified neurocomputational framework for predictive processing across cognitive domains has been revealed.
  • This mechanism links how the brain predicts and updates its understanding of the world, crucial for adaptive behavior.

Outlook:

  • Future research can explore how this mechanism is implemented in specific neural circuits.
  • Understanding this mechanism may offer insights into cognitive disorders characterized by aberrant predictive processing.