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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

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.
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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 't,' or...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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 particular...
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
Counterfactual Thinking01:19

Counterfactual Thinking

Counterfactual thinking is a cognitive process wherein individuals mentally reconstruct alternative versions of past events, often beginning with “what if” or “if only.” This reflective mechanism plays a significant role in shaping emotional experiences and guiding future behavior. Though typically triggered by unfavorable or unexpected outcomes, counterfactual thinking can also emerge in mundane, everyday decisions and experiences, revealing its deep entrenchment in human cognition.Types of...

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Using Generative Art to Convey Past and Future Climate Transitions
06:10

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Visualizing uncertainty about the future.

David Spiegelhalter1, Mike Pearson, Ian Short

  • 1Centre for Mathematical Sciences, University of Cambridge, Cambridge CB3 0WB, UK. d.spiegelhalter@statslab.cam.ac.uk

Science (New York, N.Y.)
|September 10, 2011
PubMed
Summary
This summary is machine-generated.

Communicating uncertainty visually is challenging. While interactive visualizations offer potential, effectively conveying complex or disputed uncertainties to the public remains a significant hurdle.

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Area of Science:

  • Data Visualization
  • Risk Communication
  • Cognitive Science

Background:

  • Uncertainty about the future is pervasive, with probabilities offering a way to quantify some aspects.
  • Effectively communicating these probabilities to the general public is a known challenge.
  • Visualizations are increasingly used, but evidence on their comprehension is limited.

Purpose of the Study:

  • To review current practices in visually communicating uncertainty.
  • To examine the effectiveness of different visualization types across various domains.
  • To identify challenges in communicating deeper forms of uncertainty.

Main Methods:

  • Literature review of current practices in uncertainty visualization.
  • Analysis of examples from diverse fields like weather, health, and economics.
  • Consideration of audience numeracy and interactive visualization potential.

Main Results:

  • Visual communication of uncertainty is common but lacks robust empirical support.
  • Effectiveness of visualizations is influenced by audience numeracy and graphic design.
  • Interactive and adaptable visualizations show promise for tailored communication.

Conclusions:

  • Communicating uncertainty visually requires careful consideration of audience and context.
  • Interactive visualizations can enhance understanding but do not solve all communication challenges.
  • Addressing deeper uncertainties from incomplete or disputed knowledge remains an open research area.