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Related Concept Videos

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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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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Random Error01:04

Random Error

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Probability in Statistics01:14

Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
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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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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
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Related Experiment Videos

Use and Communication of Probabilistic Forecasts.

Adrian E Raftery1

  • 1University of Washington.

Statistical Analysis and Data Mining
|April 28, 2017
PubMed
Summary

Probabilistic forecasts require tailored communication strategies. Understanding user goals and cognitive factors is key to effective use and trust in these forecasts for better decision-making.

Area of Science:

  • Decision Science
  • Cognitive Psychology
  • Forecasting

Background:

  • Probabilistic forecasts are increasingly prevalent.
  • Effective utilization and communication strategies are needed.
  • Obstacles to practical application require investigation.

Purpose of the Study:

  • To review experiences with probabilistic forecasting in diverse problems.
  • To identify distinct user types and their specific needs for probabilistic information.
  • To provide guidance on optimizing the use and communication of probabilistic forecasts.

Main Methods:

  • Review of case studies involving probabilistic forecasting.
  • Identification and categorization of user needs based on interaction and goals.
  • Analysis of cognitive research on trust, calibration, and communication of uncertainty.

Related Experiment Videos

Main Results:

  • Five user types identified: Low Stakes Users, General Assessors, Change Assessors, Risk Avoiders, and Decision Theorists.
  • User interaction and goal alignment are crucial for effective forecast utilization.
  • Calibration, matching verbal expressions to tasks, and minimizing cognitive load enhance trust.
  • Probabilities of adverse events and predictive distribution percentiles are effective summary measures.

Conclusions:

  • Tailoring probabilistic forecast communication to specific user types and goals is essential.
  • Cognitive principles, such as calibration and reduced cognitive load, improve forecast usability.
  • While formal decision theory has applications, simpler summary measures are often more practical for broader use.