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

Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

1.9K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
1.9K
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

26.2K
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
26.2K
What is a Hypothesis?01:14

What is a Hypothesis?

10.4K
A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about a  property of the population. It can be stated for any field observations or experiments. A hypothesis statement cannot be said to be right or wrong as it is merely a statement. It needs to be tested through an elaborate data collection process and an appropriate statistical test. A hypothesis should be a general but not a vague...
10.4K
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

8.0K
The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
8.0K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

183
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
183
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.0K

You might also read

Related Articles

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

Sort by
Same author

Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

IEEE transactions on cybernetics·2026
Same author

Joint Estimation of States, Sparse Sensor Attacks, and Unknown Inputs in Discrete-Time Cyber-Physical Systems.

IEEE transactions on cybernetics·2026
Same author

Improved Results for T-S Fuzzy Systems With Periodically Varying Delays via a Generalized Delay Derivative-Dependent Reciprocally Convex Matrix Inequality.

IEEE transactions on cybernetics·2026
Same author

Optimal Stealthy Attacks Against Remote State Estimation in Cyber-Physical Systems: A More Fine-Grained Stealthiness.

IEEE transactions on cybernetics·2025
Same author

Optimal Sensor Grouping Transmission Strategy for Multiple Processes Over Packet-Dropping Channels.

IEEE transactions on cybernetics·2025
Same author

Resilient Collision-Free Distributed Optimal Coordination for Multiple Euler-Lagrangian Systems Under Unreliable Communication Topologies.

IEEE transactions on cybernetics·2025
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: Jun 15, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.3K

Event-Triggered Distributed Hypothesis Testing for Multiagent Networks Based on Observations Cumulation.

Chong-Xiao Shi, Guang-Hong Yang

    IEEE Transactions on Cybernetics
    |August 28, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new distributed hypothesis testing algorithm for multiagent networks using historical observations. The novel approach ensures reliable convergence, improving upon existing methods by removing strict parameter constraints.

    More Related Videos

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.3K
    A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
    06:28

    A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

    Published on: August 26, 2018

    6.0K

    Related Experiment Videos

    Last Updated: Jun 15, 2025

    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

    9.3K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.3K
    A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
    06:28

    A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

    Published on: August 26, 2018

    6.0K

    Area of Science:

    • Distributed systems
    • Signal processing
    • Control theory

    Background:

    • Multiagent networks face challenges in distributed hypothesis testing.
    • Existing algorithms often require specific parameter tuning for convergence.
    • Event-triggered communication is crucial for efficient information exchange.

    Purpose of the Study:

    • To propose a novel event-triggered distributed hypothesis testing algorithm.
    • To enhance algorithm convergence by incorporating cumulation of historical observations.
    • To provide a theoretical guarantee of convergence independent of parameter selection.

    Main Methods:

    • Development of an event-triggered distributed hypothesis testing algorithm.
    • Integration of historical observation cumulation into the algorithm's framework.
    • Theoretical analysis to prove convergence properties and derive convergence rates.

    Main Results:

    • The proposed algorithm guarantees convergence regardless of event-triggered parameter choices.
    • This represents an advancement over existing methods with stricter parameter dependencies.
    • An explicit convergence rate for the new algorithm is mathematically derived.

    Conclusions:

    • The novel algorithm effectively addresses distributed hypothesis testing in multiagent networks.
    • Cumulation of historical observations significantly improves algorithm robustness and convergence.
    • Simulation results validate the practical effectiveness of the proposed approach.