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Uncertainty: Overview00:59

Uncertainty: Overview

1.8K
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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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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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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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
7.1K
The Uncertainty Principle04:08

The Uncertainty Principle

33.6K
Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
33.6K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.5K
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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Related Experiment Video

Updated: Mar 2, 2026

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

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Uncertainty quantification and optimal decisions.

C L Farmer1

  • 1OCIAM, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK.

Proceedings. Mathematical, Physical, and Engineering Sciences
|May 10, 2017
PubMed
Summary
This summary is machine-generated.

This study reviews an ideal workflow for creating optimal decision-making policies using mathematical models. It emphasizes uncertainty quantification and long-term benefits for robust, real-world applications.

Keywords:
forecastingstochastic controluncertainty quantification

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Area of Science:

  • Applied Mathematics
  • Decision Science
  • Operations Research

Background:

  • Mathematical models are crucial for developing optimal policies.
  • Model accuracy is key to successful real-world implementation.
  • Diverse fields like oil, weather, and agriculture benefit from these models.

Purpose of the Study:

  • To review the ideal workflow for constructing optimal decision-making policies.
  • To highlight the importance of uncertainty quantification in forecasting.
  • To demonstrate robust decision-making strategies under model uncertainty.

Main Methods:

  • Review of an ideal workflow encompassing modeling, forecasting, and data assimilation.
  • Emphasis on uncertainty quantification in forecasting processes.
  • Optimization of decision-making policies considering long-term costs and benefits.

Main Results:

  • Mathematical models can yield near-optimal policies for real-world action.
  • Uncertainty quantification is vital for robust forecasting and decision-making.
  • Long-term perspectives and data utilization lead to significantly different policy recommendations.

Conclusions:

  • An integrated workflow of modeling, forecasting, and decision optimization is essential.
  • Robustness to uncertainty is achieved through comprehensive data assimilation and analysis.
  • Balancing long-term costs and benefits can redefine optimal strategies.