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

Decision Making: P-value Method01:09

Decision Making: P-value Method

7.4K
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...
7.4K
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

7.9K
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
7.9K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

12.7K
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...
12.7K
Self-Evaluation Maintenance Model01:29

Self-Evaluation Maintenance Model

404
The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
404
Unrealistic Optimism Bias01:30

Unrealistic Optimism Bias

347
Unrealistic optimism bias is the tendency to overestimate the likelihood of positive outcomes. This cognitive bias makes individuals believe they are less likely to experience failures, setbacks, or risks and more likely to succeed than others. For example, people may assume they are less prone to health issues, accidents, or financial struggles than their peers, even when they share similar risk factors.One key component of this bias is the above-average effect, where individuals perceive...
347
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

30.1K
The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null...
30.1K

You might also read

Related Articles

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

Sort by
Same author

An Incentive Mechanism-Based Minimum Adjustment Consensus Model Under Dynamic Trust Relationship.

IEEE transactions on cybernetics·2024
Same author

Multimodal Approach for Enhancing Biometric Authentication.

Journal of imaging·2023
Same author

CD-BFT: Canonical Decomposition-Based Belief Functions Transformation in Possibility Theory.

IEEE transactions on cybernetics·2023
Same author

Self-supervised learning for remote sensing scene classification under the few shot scenario.

Scientific reports·2023
Same author

An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making.

Artificial intelligence review·2022
Same author

Fermatean fuzzy copula aggregation operators and similarity measures-based complex proportional assessment approach for renewable energy source selection.

Complex & intelligent systems·2022
Same journal

A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths.

IEEE transactions on cybernetics·2026
Same journal

Robust Semiglobal and Global Stabilization for Nonlinear Normal Form Systems by Time-Varying Feedback.

IEEE transactions on cybernetics·2026
Same journal

Adaptive Global Asymptotic Output Stabilization of Uncertain Nonlinear Systems Under Dynamic State/Input Quantization.

IEEE transactions on cybernetics·2026
Same journal

Accelerated Distributed Gradient Tracking for Constrained Aggregative Optimization Over Time-Varying Digraphs.

IEEE transactions on cybernetics·2026
Same journal

Small-Gain-Based Plug-and-Play Distributed Control Framework for DC Microgrids With Decentralized Reconfiguration.

IEEE transactions on cybernetics·2026
Same journal

Prescribed-Time Impulsive Control of High-Order Integrator Systems.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Apr 14, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.5K

Evaluating Belief Structure Satisfaction to Uncertain Target Values.

Ronald R Yager, Naif Alajlan

    IEEE Transactions on Cybernetics
    |April 17, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Dempster-Shafer belief structures for modeling imprecise probabilities. It extends belief and plausibility measures to evaluate complex uncertain targets, enhancing probabilistic information modeling.

    More Related Videos

    Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses
    06:42

    Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

    Published on: September 28, 2018

    12.4K
    Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
    06:45

    Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

    Published on: April 18, 2017

    6.7K

    Related Experiment Videos

    Last Updated: Apr 14, 2026

    Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
    13:04

    Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

    Published on: September 19, 2012

    12.5K
    Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses
    06:42

    Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

    Published on: September 28, 2018

    12.4K
    Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
    06:45

    Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal

    Published on: April 18, 2017

    6.7K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Information Theory

    Background:

    • Dempster-Shafer theory provides a framework for reasoning under uncertainty.
    • Belief structures model imprecise probabilistic information.
    • Current methods for evaluating targets within belief structures are limited.

    Purpose of the Study:

    • To describe the fundamental properties of Dempster-Shafer belief structures.
    • To introduce and define measures of belief and plausibility.
    • To extend these measures for evaluating complex uncertain targets.

    Main Methods:

    • Describing basic properties of Dempster-Shafer belief structures.
    • Defining belief and plausibility measures.
    • Developing methods to calculate target satisfaction by belief structures.
    • Extending measures to handle probability distributions, other belief structures, measures, and possibility distributions.

    Main Results:

    • The study details the foundational aspects of Dempster-Shafer belief structures.
    • It introduces novel extensions of belief and plausibility measures.
    • These extended measures can quantify satisfaction for complex uncertain targets, including probability and possibility distributions.

    Conclusions:

    • Dempster-Shafer belief structures offer a robust model for imprecise probabilistic information.
    • The extended belief and plausibility measures provide a formal mechanism for evaluating complex uncertain targets.
    • This work advances the application of Dempster-Shafer theory in uncertainty reasoning.