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

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
359
Cause and Effect01:53

Cause and Effect

10.5K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
10.5K
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

5.8K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
5.8K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

594
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
594
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.0K
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...
6.0K

You might also read

Related Articles

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

Sort by
Same author

Rumor propagation and supervision during confrontation: An importance-driven SIRQS network model.

Chaos (Woodbury, N.Y.)·2026
Same author

Unintended consequences of well-intended interventions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Higher-order interactions shape collective human behaviour.

Nature human behaviour·2025
Same author

Counterfeit judgments in large language models.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

The impact of feedbacks on evolutionary game dynamics in structured populations.

Chaos (Woodbury, N.Y.)·2025
Same author

Two-by-two ordinal patterns in art paintings.

PNAS nexus·2025
Same journal

RNA-ligand complexes and the attenuation of neutral confinement in the evolution of RNA secondary structures.

Journal of the Royal Society, Interface·2026
Same journal

Individual detachment-reintegration events in homing pigeon flocks and the dominance of directional adjustment in their kinematic features.

Journal of the Royal Society, Interface·2026
Same journal

Thermal stress disrupts symbiotic fluid dynamics in bobtail squid.

Journal of the Royal Society, Interface·2026
Same journal

Distinct geometrical landscapes distinguish between modes of tristability in gene regulatory networks.

Journal of the Royal Society, Interface·2026
Same journal

Slow modulation of the contraction patterns in Physarum polycephalum.

Journal of the Royal Society, Interface·2026
Same journal

Moo-ving mountains: grazing agents drive terracette formation on steep hillslopes.

Journal of the Royal Society, Interface·2026
See all related articles

Related Experiment Video

Updated: Apr 27, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.3K

The Matthew effect in empirical data.

Matjaž Perc1

  • 1Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000 Maribor, Slovenia matjaz.perc@uni-mb.si.

Journal of the Royal Society, Interface
|July 4, 2014
PubMed
Summary
This summary is machine-generated.

The Matthew effect shows that advantages accumulate, leading to increasing inequality in various systems. This phenomenon, related to preferential attachment, is observed across science, technology, and biology.

Keywords:
Matthew effectcumulative advantageempirical datapower lawpreferential attachmentself-organization

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.1K
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

2.1K

Related Experiment Videos

Last Updated: Apr 27, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.3K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.1K
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

2.1K

Area of Science:

  • Network Science
  • Sociology
  • Biology
  • Education

Background:

  • The Matthew effect describes how advantages tend to amplify over time, a concept mirrored in preferential attachment within network science.
  • Related concepts include cumulative advantage and success-breeds-success, highlighting the self-reinforcing nature of early gains.
  • This phenomenon underpins power laws and scaling behaviors observed in diverse empirical datasets.

Purpose of the Study:

  • To review methodologies for quantifying preferential attachment in empirical data.
  • To survey the manifestations of the Matthew effect across various scientific and social domains.
  • To explore the underlying mechanisms (chance vs. optimization) driving the Matthew effect.

Main Methods:

  • Review of existing literature on preferential attachment measurement.
  • Analysis of empirical data from scientific collaboration, socio-technical, biological networks, and citation patterns.
  • Examination of case studies in scientific progress, career longevity, language evolution, education, and brain development.

Main Results:

  • The Matthew effect is widely observed in scientific collaboration, citation networks, and the emergence of scientific impact.
  • Evidence suggests its presence in socio-technical systems, biological networks, word evolution, and developmental processes.
  • The review synthesizes diverse methodologies for detecting and measuring this cumulative advantage phenomenon.

Conclusions:

  • The Matthew effect, driven by preferential attachment, is a fundamental self-organization principle across diverse systems.
  • Understanding its mechanisms, whether chance or optimization (e.g., homophily, efficacy), is crucial for future research.
  • Further investigation is needed to fully elucidate the drivers and implications of cumulative advantage in various contexts.