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

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

929
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
929
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

300
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
300
Randomized Experiments01:13

Randomized Experiments

7.1K
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...
7.1K
Law of Independent Assortment02:03

Law of Independent Assortment

56.2K
While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
56.2K
Random Error01:04

Random Error

1.4K
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...
1.4K
Random Variables01:09

Random Variables

13.3K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
13.3K

You might also read

Related Articles

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

Sort by
Same author

Clinical outcomes of patients with diffuse large B-cell lymphoma and concomitant rheumatoid arthritis: a nationwide Danish register-based cohort study.

EULAR rheumatology open·2026
Same author

PROTOCOL: Utilisation of Genetics and Genomics in Primary Care: Protocol for an Evidence and Gap Map.

Campbell systematic reviews·2026
Same author

Mosaic Chromosomal Alterations Identify Ultra-High Risk Clonal Hematopoiesis in Patients With Lymphoma Undergoing Intensive Chemotherapy.

American journal of hematology·2026
Same author

Development and Validation of a Novel Conditional Event-Free Survival Tool in Diffuse Large B-Cell Lymphoma.

American journal of hematology·2026
Same author

CD20 negativity at the start of second-line therapy predicts a shorter overall survival in B-cell lymphomas.

Blood neoplasia·2026
Same author

A targeted circulating tumor DNA landscape of copy number aberrations in large B-cell lymphomas.

Leukemia·2026

Related Experiment Video

Updated: Aug 24, 2025

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light
09:19

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

Published on: July 29, 2013

11.5K

Tight Analytic Bound on the Trade-Off between Device-Independent Randomness and Nonlocality.

Lewis Wooltorton1,2, Peter Brown3, Roger Colbeck1

  • 1Department of Mathematics, University of York, Heslington, York YO10 5DD, United Kingdom.

Physical Review Letters
|October 21, 2022
PubMed
Summary

Researchers derived bounds on certifiable randomness from quantum correlations. They found maximal randomness is possible for certain Clauser-Horne-Shimony-Holt (CHSH) values, providing an upper bound for device-independent random number generation.

More Related Videos

Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials
10:35

Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials

Published on: September 26, 2014

12.4K
Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
15:06

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

12.9K

Related Experiment Videos

Last Updated: Aug 24, 2025

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light
09:19

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

Published on: July 29, 2013

11.5K
Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials
10:35

Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials

Published on: September 26, 2014

12.4K
Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
15:06

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

12.9K

Area of Science:

  • Quantum Information Theory
  • Quantum Foundations

Background:

  • Quantum entanglement enables correlations beyond classical limits.
  • Nonlocal correlations are crucial for device-independent random number generation.
  • Quantifying certifiable randomness from nonlocal correlations is essential.

Purpose of the Study:

  • Derive tight analytic bounds on maximum certifiable randomness.
  • Relate certifiable randomness to the Clauser-Horne-Shimony-Holt (CHSH) value.
  • Establish achievable upper bounds for randomness generation.

Main Methods:

  • Analytic derivation of bounds on certifiable randomness.
  • Analysis as a function of the CHSH value.
  • Utilizing Bell inequalities and quantum correlations.

Main Results:

  • Maximal two bits of global randomness are certifiable for CHSH values up to approximately 2.598.
  • Maximum certifiable randomness decreases for CHSH values beyond this threshold.
  • A new family of Bell inequalities provides optimal randomness certification for higher CHSH values.

Conclusions:

  • Provides an achievable upper bound for randomness certification based on CHSH values.
  • Demonstrates robustness using a Werner state noise model.
  • Offers insights for improving practical random number generation rates.