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

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

10.9K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
10.9K
Fundamental Attribution Error01:14

Fundamental Attribution Error

13.7K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.7K
Random Error01:04

Random Error

9.7K
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...
9.7K
Margin of Error01:27

Margin of Error

7.6K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.6K
Contaminants and Errors01:16

Contaminants and Errors

367
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
367
Standard Error of the Mean01:13

Standard Error of the Mean

12.3K
The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
12.3K

You might also read

Related Articles

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

Sort by
Same author

A GATA transcription factor OfGATA9 positively regulates flower size of sweet osmanthus.

BMC genomics·2025
Same author

Multi-omics analysis of potential metabolic networks linking peripheral metabolic changes to inflammatory retinal conditions in STZ-induced early diabetic retinopathy.

Biochemistry and biophysics reports·2025
Same author

Multi-omics profiling reveals liver-retina axis in high-fat diet-induced early retinopathy.

Biochemical and biophysical research communications·2025
Same author

Achieving Extra-High-Quality Images in ToF-SIMS Molecular Imaging of Insulating Samples Using a Low Current O<sub>2</sub><sup>+</sup> Auxiliary Beam and Back Side Au Coating.

Analytical chemistry·2025
Same author

Pt(OH)<sub></sub> Coordination Engineering of Bifunctional Pt/NiFe LDH-O Catalyst for Robust Water Splitting.

ACS applied materials & interfaces·2025
Same author

Integrating Competitive Li<sup>+</sup> Coordination with Immobilized Anions in Composite Solid Electrolyte for High-Performance Li Metal Batteries.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same journal

The infimum values of two probability functions for the Gamma distribution.

Journal of inequalities and applications·2024
Same journal

The existence of nonnegative solutions for a nonlinear fractional <i>q</i>-differential problem via a different numerical approach.

Journal of inequalities and applications·2021
Same journal

Correction to: On the spectral norms of <i>r</i>-circulant matrices with the bi-periodic Fibonacci and Lucas numbers.

Journal of inequalities and applications·2019
Same journal

Erratum to: General Bahr-Esseen inequalities and their applications.

Journal of inequalities and applications·2019
Same journal

Hermite-Hadamard type inequalities for <i>F</i>-convex function involving fractional integrals.

Journal of inequalities and applications·2019
Same journal

Global maximal inequality to a class of oscillatory integrals.

Journal of inequalities and applications·2019
See all related articles

Related Experiment Video

Updated: Jan 28, 2026

Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

9.1K

Higher-order error bound for the difference of two functions.

Hui Huang1, Mengxue Xia1

  • 1Department of Mathematics, Yunnan University, Kunming, P.R. China.

Journal of Inequalities and Applications
|March 7, 2019
PubMed
Summary
This summary is machine-generated.

Researchers established higher-order error bounds for difference functions using nonsmooth analysis techniques. These findings are crucial for advancing mathematical programming research and understanding error propagation in constrained optimization problems.

Keywords:
Difference functionEkeland variational principleError bound

More Related Videos

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.5K
Assessing Differences in Sperm Competitive Ability in Drosophila
09:34

Assessing Differences in Sperm Competitive Ability in Drosophila

Published on: August 22, 2013

15.0K

Related Experiment Videos

Last Updated: Jan 28, 2026

Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

9.1K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.5K
Assessing Differences in Sperm Competitive Ability in Drosophila
09:34

Assessing Differences in Sperm Competitive Ability in Drosophila

Published on: August 22, 2013

15.0K

Area of Science:

  • Mathematical Optimization
  • Nonsmooth Analysis

Background:

  • Error bounds are fundamental in mathematical programming.
  • Understanding error bounds is key to analyzing the convergence and stability of algorithms.

Purpose of the Study:

  • To establish the existence of higher-order error bounds for difference functions.
  • To extend existing error bound theory to problems with set constraints.

Main Methods:

  • Application of nonsmooth analysis techniques.
  • Development of novel theoretical frameworks for error bound analysis.

Main Results:

  • Demonstrated the existence of higher-order error bounds for difference functions under set constraints.
  • Provided new theoretical insights into the behavior of error bounds in constrained optimization.

Conclusions:

  • The study contributes significant theoretical advancements to mathematical programming.
  • The established results offer a foundation for developing more robust optimization algorithms.