Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

1.3K
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.
1.3K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

8.3K
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...
8.3K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.4K
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...
1.4K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

98.2K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
98.2K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.1K
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...
1.1K
The Uncertainty Principle04:08

The Uncertainty Principle

30.0K
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...
30.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

WHO estimates of the global, regional, and national foodborne burdens of 14 invasive parasitic diseases, 2000-21: an updated data synthesis.

The Lancet. Global health·2026
Same author

WHO estimates of the global, regional, and national disease burden of nine foodborne chemicals, 2000-21: an updated data synthesis.

The Lancet. Global health·2026
Same author

WHO estimates of the global, regional, and national burden of eight foodborne non-diarrhoeal enteric disease hazards, 2000-21: an updated data synthesis.

The Lancet. Global health·2026
Same author

WHO estimates of the global, regional, and national burden of 14 foodborne diarrhoeal enteric hazards, 2000-21: an updated data synthesis.

The Lancet. Global health·2026
Same author

WHO estimates of the global, regional, and national burden of 42 foodborne infectious and chemical hazards, 2000-21: an updated data synthesis.

The Lancet. Global health·2026
Same author

Source attribution of foodborne pathogens in the Netherlands using structured expert elicitation.

International journal of food microbiology·2026
Same journal

A Hybrid FMEA-AHP Framework for Risk Prioritization in Nontransparent Artificial Intelligence Systems.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Trust-Building Communication for Extreme Heat Preparedness.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Spring Broken: A Risk Analysis of Fatal and Nonfatal Traffic Injuries in Florida.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Global Sensitivity Analysis of Societal Resilience Using Shapley Values and Polynomial Chaos Expansion.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Assessing How Fact-Checks Influence Accuracy and Consensus Judgments: Evidence From the Olympics.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Applying the Bow Tie Method to Evaluate Emerging Risk: The Case of Carbon Capture and Water Stress.

Risk analysis : an official publication of the Society for Risk Analysis·2026
See all related articles

Related Experiment Video

Updated: Nov 16, 2025

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
09:07

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

9.3K

Uncertainty Quantification with Experts: Present Status and Research Needs.

Anca M Hanea1, Victoria Hemming2, Gabriela F Nane3

  • 1Centre of Excellence for Biosecurity Risk Analysis, The University of Melbourne, Melbourne, Victoria, Australia.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|February 25, 2021
PubMed
Summary
This summary is machine-generated.

Expert elicitation guides critical decisions with limited data. This guide helps practitioners navigate design choices for better data quality, expert engagement, and defensible results.

Keywords:
CMIDEASHELFexpert elicitation protocolsuncertainty quantification

More Related Videos

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.5K
Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

15.5K

Related Experiment Videos

Last Updated: Nov 16, 2025

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
09:07

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

9.3K
Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.5K
Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

15.5K

Area of Science:

  • Decision Science
  • Cognitive Psychology
  • Risk Analysis

Background:

  • Expert elicitation is crucial for decision-making when data is scarce or unreliable.
  • Practitioners face numerous design choices that impact elicitation outcomes.
  • Balancing best practices with practical constraints is a common challenge.

Purpose of the Study:

  • To outline common design choices in expert elicitation.
  • To discuss the evidence supporting these choices.
  • To identify research gaps in expert judgment methodology.

Main Methods:

  • Review of common expert elicitation design decisions.
  • Discussion of supporting evidence and identified research gaps.
  • Focus on practical constraints and best practice trade-offs.

Main Results:

  • Identification of key decision points in designing expert elicitation.
  • Analysis of how choices affect data quality, expert engagement, and result defensibility.
  • Highlighting areas needing further research for improved elicitation practices.

Conclusions:

  • Informed design choices enhance expert elicitation effectiveness.
  • Addressing identified research gaps will advance expert judgment methodology.
  • Better navigation of the literature supports practitioners in making defensible decisions.