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

Group Design02:01

Group Design

10.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
10.9K
Randomized Experiments01:13

Randomized Experiments

9.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.1K
Random Error01:04

Random Error

9.9K
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...
9.9K
Contaminants and Errors01:16

Contaminants and Errors

432
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...
432
Random Sampling Method01:09

Random Sampling Method

15.3K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
15.3K
Random and Systematic Errors01:20

Random and Systematic Errors

15.6K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
15.6K

You might also read

Related Articles

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

Sort by
Same author

Rare trajectories in a prototypical mean-field disordered model: Insights into landscape and instantons.

Physical review. E·2026
Same author

Sampling the space of solutions of an artificial neural network.

Physical review. E·2025
Same author

Star-Shaped Space of Solutions of the Spherical Negative Perceptron.

Physical review letters·2023
Same author

Typical and atypical solutions in nonconvex neural networks with discrete and continuous weights.

Physical review. E·2023
Same author

Planted matching problems on random hypergraphs.

Physical review. E·2022
Same author

Learning through atypical phase transitions in overparameterized neural networks.

Physical review. E·2022
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Feb 28, 2026

Operant Procedures for Assessing Behavioral Flexibility in Rats
08:30

Operant Procedures for Assessing Behavioral Flexibility in Rats

Published on: February 15, 2015

21.7K

Finite-size corrections in the random assignment problem.

Sergio Caracciolo1, Matteo P D'Achille1, Enrico M Malatesta1

  • 1Dipartimento di Fisica, University of Milan and INFN, Via Celoria 16, 20133 Milan, Italy.

Physical Review. E
|June 17, 2017
PubMed
Summary
This summary is machine-generated.

This study analyzes finite-size corrections in the random assignment problem. Changing cost distributions significantly alters optimal cost corrections, confirmed by simulations.

More Related Videos

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

9.3K
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: Feb 28, 2026

Operant Procedures for Assessing Behavioral Flexibility in Rats
08:30

Operant Procedures for Assessing Behavioral Flexibility in Rats

Published on: February 15, 2015

21.7K
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

9.3K
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:

  • Statistical Physics
  • Combinatorial Optimization

Background:

  • The random assignment problem is a fundamental combinatorial optimization problem.
  • Understanding finite-size effects is crucial for accurate theoretical predictions.

Purpose of the Study:

  • To analytically derive the first finite-size corrections to the average optimal cost.
  • To investigate the impact of different cost distribution laws on these corrections.

Main Methods:

  • Utilizing the replica formalism for analytical derivation.
  • Solving saddle-point equations numerically.
  • Performing numerical simulations to validate theoretical predictions.

Main Results:

  • The leading finite-size correction's sign and scaling properties change when transitioning from power-law to Gamma distributions.
  • The behavior of corrections near a delta-function distribution was analyzed.

Conclusions:

  • The choice of cost distribution law critically influences finite-size corrections in the random assignment problem.
  • Analytical and numerical methods confirm the theoretical findings.