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相关概念视频

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

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想象对未来的不确定性,对未来的不确定性.

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
概括
此摘要是机器生成的。

在视觉上传达不确定性是一个挑战. 虽然交互式可视化提供了潜力,但有效地向公众传达复杂或有争议的不确定性仍然是一个重大障碍.

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13:04

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Published on: September 19, 2012

相关实验视频

Last Updated: May 29, 2026

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科学领域:

  • 数据可视化 数据可视化
  • 风险沟通 风险沟通
  • 认知科学 认知科学

背景情况:

  • 关于未来的不确定性是普遍存在的,概率提供了一种方法来量化某些方面.
  • 将这些概率有效地传达给公众是一个已知的挑战.
  • 视觉化越来越多地使用,但对其理解的证据是有限的.

研究的目的:

  • 审查目前在视觉传播不确定性的做法.
  • 检查不同领域的不同可视化类型的有效性.
  • 识别沟通更深层次的不确定性的挑战.

主要方法:

  • 对不确定性可视化当前实践的文献综述.
  • 分析来自不同领域的例子,如天气,健康和经济学.
  • 考虑观众的计算能力和交互式可视化潜力.

主要成果:

  • 不确定性的视觉传播是常见的,但缺乏强大的经验支持.
  • 视觉化的有效性受到观众的计算能力和图形设计的影响.
  • 交互式和可适应的可视化显示为量身定制的沟通有希望.

结论:

  • 在视觉上传达不确定性需要仔细考虑观众和背景.
  • 交互式可视化可以增强理解,但并不能解决所有沟通挑战.
  • 从不完整或有争议的知识中解决更深层次的不确定性仍然是一个开放的研究领域.