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Uncertainty: Overview

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 Intervals

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 Error

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 Error

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 Assessment

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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 Thinking

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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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Visualizar la incertidumbre sobre el futuro.

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

Resumen

Este resumen es generado por máquina.

Comunicar la incertidumbre visualmente es un desafío. Si bien las visualizaciones interactivas ofrecen un potencial, transmitir efectivamente incertidumbres complejas o disputadas al público sigue siendo un obstáculo significativo.

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Área de la Ciencia:

Visualización de datos y visualización de datos.
Comunicación del riesgo Comunicación del riesgo.
Ciencias Cognitivas Ciencias Cognitivas.

Sus antecedentes:

La incertidumbre sobre el futuro es generalizada, y las probabilidades ofrecen una forma de cuantificar algunos aspectos.
Comunicar efectivamente estas probabilidades al público en general es un desafío conocido.
Las visualizaciones se utilizan cada vez más, pero la evidencia sobre su comprensión es limitada.

Objetivo del estudio:

Revisar las prácticas actuales en la comunicación visual de la incertidumbre.
Examinar la efectividad de diferentes tipos de visualización en varios dominios.
Para identificar los desafíos en la comunicación de formas más profundas de incertidumbre.

Principales métodos:

Revisión de la literatura de las prácticas actuales en la visualización de la incertidumbre.
Análisis de ejemplos de diversos campos como el clima, la salud y la economía.
La consideración de la capacidad numérica de la audiencia y el potencial de visualización interactiva.

Principales resultados:

La comunicación visual de la incertidumbre es común, pero carece de un soporte empírico sólido.
La efectividad de las visualizaciones está influenciada por la capacidad numérica de la audiencia y el diseño gráfico.
Las visualizaciones interactivas y adaptables son prometedoras para una comunicación personalizada.

Conclusiones:

Comunicar la incertidumbre visualmente requiere una cuidadosa consideración de la audiencia y el contexto.
Las visualizaciones interactivas pueden mejorar la comprensión, pero no resuelven todos los desafíos de comunicación.
Abordar las incertidumbres más profundas de los conocimientos incompletos o disputados sigue siendo un área de investigación abierta.