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

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

10.3K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
10.3K
Confidence Intervals01:21

Confidence Intervals

11.0K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
11.0K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

11.9K
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...
11.9K
Confidence Coefficient01:24

Confidence Coefficient

10.8K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
10.8K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

9.1K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
9.1K
Probability Histograms01:17

Probability Histograms

13.4K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
13.4K

You might also read

Related Articles

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

Sort by
Same author

Multimodal MRI marker of cognition explains the association between cognition and mental health in the UK Biobank.

eLife·2026
Same author

Machine learning-based identification of abnormal functional connectivity in obesity across different metabolic states.

Communications medicine·2026
Same author

EEG connectivity features associated with fibromyalgia revealed by machine learning.

Frontiers in pain research (Lausanne, Switzerland)·2026
Same author

Explicit semantic guided bi-incomplete multi-modal hashing with label co-occurrence and label graph constraints.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

AMLPF-CLIP: Adaptive Prompting and Distilled Learning for Imbalanced Histopathological Image Classification.

IEEE journal of biomedical and health informatics·2025
Same author

Quantum granular-ball generation methods and their application in KNN classification.

Scientific reports·2025
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
Same journal

Relaxed Stability Conditions for Model Predictive Control of Hybrid Dynamical Systems Using Hybrid Recurrent Neural Networks.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Mar 7, 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

Benchmarking Stochastic Algorithms for Global Optimization Problems by Visualizing Confidence Intervals.

Qunfeng Liu, Wei-Neng Chen, Jeremiah D Deng

    IEEE Transactions on Cybernetics
    |February 11, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel, distribution-free method for benchmarking stochastic optimization algorithms. It uses confidence intervals and performance profiles for statistically sound comparisons, considering both means and variances.

    More Related Videos

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    3.0K
    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

    Related Experiment Videos

    Last Updated: Mar 7, 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 Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    3.0K
    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

    Area of Science:

    • Optimization
    • Computer Science
    • Statistics

    Background:

    • Traditional benchmarking for deterministic algorithms uses performance and data profiles.
    • Significance tests in traditional methods face increasing criticism.
    • Existing methods for stochastic optimization often rely on sample means only.

    Purpose of the Study:

    • To extend performance and data profiles for benchmarking stochastic global optimization algorithms.
    • To introduce a statistically robust and distribution-free benchmarking approach.
    • To enable visual comparison of stochastic optimization algorithms using graphs.

    Main Methods:

    • Employing a general confidence interval instead of significance tests.
    • Computing confidence bounds and visualizing them with performance/data profiles.
    • Developing a statistically synthetic method suitable for large benchmark sets.

    Main Results:

    • The proposed method considers both sample means and variances.
    • It is a distribution-free method, not requiring population distribution assumptions.
    • The graphical visualization aids in comparing stochastic optimization algorithms.

    Conclusions:

    • The novel benchmarking method offers a statistically sound and flexible approach.
    • It is particularly suitable for comparing stochastic optimization algorithms across diverse problems.
    • The distribution-free nature enhances its applicability and reliability.