Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

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...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...

Random Error

Random Error

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...

You might also read

Related Articles

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

Sort by

Same author

Continuous-Flow Ammoxidation of 2-Methylpyrazine to 2-Cyanopyrazine with High Space-Time Yield in a Microreactor.

ACS omega·2022

Same author

Comprehensive disposal plan design & polymerization disaster risk assessment based on information diffusion technology in Fujiang River basin in Sichuan.

Environmental research·2020

Same author

Two judging criteria to check validity of a model for filling gaps caused by incomplete geospatial data.

Environmental research·2020

Same author

Demulsification of Kerosene/Water Emulsion in the Transparent Asymmetric Plate-Type Micro-Channel.

Micromachines·2018

Same author

An information diffusion technique to assess integrated hazard risks.

Environmental research·2017

Same author

To facilitate the advance of risk analysis and crisis response in China.

Environmental research·2016

Same journal

Competition and Collaboration in the AI Race: Country-LevelDirectional Evidence for Risk Monitoring and Policy.

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

Same journal

Cyber Resilience: Management With Cybersecurity Controls.

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

Same journal

Jack Fowle: Combining Values, Experience, and Teamwork to Improve Risk Analysis.

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

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 3, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Fuzzy risks and an updating algorithm with new observations.

Chongfu Huang¹, Da Ruan

¹State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China. hchongfu@bnu.edu.cn

Risk Analysis : an Official Publication of the Society for Risk Analysis

|July 23, 2008

Summary

This summary is machine-generated.

This study defines risk as a future scene with potential adverse incidents. It introduces fuzzy risk and a fuzzy average algorithm to update risk information, demonstrated with a flood risk map.

Related Experiment Videos

Last Updated: Jul 3, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Risk analysis
Fuzzy set theory
Environmental science

Background:

Traditional risk definitions struggle with uncertainty and limited knowledge.
Perception of risk is often imprecise due to system complexity.
Adverse incidents in the future are inherently uncertain.

Purpose of the Study:

To propose a novel definition of risk focusing on future adverse incidents.
To introduce the concept of fuzzy risk using fuzzy set theory.
To develop and illustrate a fuzzy average algorithm for updating fuzzy risk information.

Main Methods:

Definition of risk as a future scene associated with an adverse incident.
Application of fuzzy set theory to model imprecise risk perception.
Development of a fuzzy average algorithm for updating fuzzy risk data.
Utilizing the interior-outer-set model for fuzzy risk calculation.

Main Results:

A new conceptualization of risk is presented, addressing its inherent fuzziness.
A fuzzy average algorithm effectively updates fuzzy risk information, preserving original data.
The algorithm's application to a flood risk map demonstrates its practical utility.

Conclusions:

The proposed definition and fuzzy risk concept enhance understanding of uncertain future events.
The fuzzy average algorithm provides a robust method for managing and updating risk information.
This approach offers improved risk assessment capabilities, particularly for environmental hazards like floods.