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

1.8K
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.
1.8K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.0K
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...
2.0K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

11.8K
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...
11.8K
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

339
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...
339
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...
1.5K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

109.5K
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. 
109.5K

You might also read

Related Articles

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

Sort by
Same author

On context specificity and management reasoning: moving beyond diagnosis.

Diagnosis (Berlin, Germany)·2025
Same author

The Clinical Frailty Scale can be used retrospectively to assess the frailty of patients with hip fracture: a validation study.

European geriatric medicine·2022
Same author

Virtual Wards: A Rapid Adaptation to Clinical Attachments in MBChB During the COVID-19 Pandemic.

Advances in experimental medicine and biology·2022
Same author

The influence of different aspects of grouse moorland management on nontarget bird assemblages.

Ecology and evolution·2019
Same author

Safety of Extubating Mechanically Ventilated Patients Receiving Vasoactive Infusions: A Retrospective Cohort Study.

American journal of respiratory and critical care medicine·2018
Same author

CowPI: A Rumen Microbiome Focussed Version of the PICRUSt Functional Inference Software.

Frontiers in microbiology·2018

Related Experiment Video

Updated: Feb 21, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

A Framework for Understanding Uncertainty in Seismic Risk Assessment.

Roxane Foulser-Piggott1, Gary Bowman2, Martin Hughes3

  • 1School of Mathematics and Physics, University of Queensland, Brisbane, Queensland, Australia.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|October 13, 2017
PubMed
Summary

Understanding uncertainty in seismic risk models is key for earthquake safety decisions. This study introduces a new framework to better quantify and communicate these uncertainties, improving risk assessments for buildings globally.

Keywords:
Aleatory and epistemic uncertaintyannual probability of collapserisk assessmentseismic hazardsensitivity analysis

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.7K
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

15.8K

Related Experiment Videos

Last Updated: Feb 21, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.7K
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

15.8K

Area of Science:

  • Earthquake Engineering
  • Risk Assessment
  • Seismology

Background:

  • Current seismic risk models inadequately address uncertainty in collapse probability estimations.
  • Improved understanding of uncertainty is crucial for effective earthquake safety decision-making.

Purpose of the Study:

  • To present a novel model framework for enhancing seismic risk assessment by comprehensively treating uncertainty.
  • To provide decision-makers with a clearer understanding of the limitations in seismic risk estimates.

Main Methods:

  • Developed a methodology for novel treatment of uncertainties in input variables and their propagation.
  • Conducted a global sensitivity analysis to identify significant uncertain variables.
  • Applied the model to case studies of buildings globally with varying seismicity and vulnerability.

Main Results:

  • Uncertainty in ground-motion conversion equations significantly impacts annual collapse probability calculations.
  • Building vulnerability influences the range of uncertainty in estimated annual collapse probabilities.
  • Less vulnerable buildings exhibit smaller uncertainty ranges in collapse probability estimates.

Conclusions:

  • The proposed framework enhances seismic risk assessment by quantifying uncertainty.
  • Accurate data acquisition and understanding model limitations are vital for reliable risk management.
  • The findings aid in preventing over- or underestimation of seismic risks for buildings.