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

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.1K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
4.1K
Reliability and Validity01:29

Reliability and Validity

14.3K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
14.3K
Probability Laws01:49

Probability Laws

44.8K
Overview
44.8K
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

570
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
570
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

7.1K
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...
7.1K
Statistical Significance01:37

Statistical Significance

23.8K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
23.8K

You might also read

Related Articles

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

Sort by
Same author

An efficient LASSO framework for admixture-aware polygenic scores.

HGG advances·2026
Same author

Variable selection for explaining interindividual heterogeneity in longitudinal growth trajectories.

Psychological methods·2026
Same author

Effects of daily low oxygen exposure on weight status, body composition, and metabolic health in adults with obesity: protocol for a randomized, double-blind, controlled-feeding study.

Contemporary clinical trials·2025
Same author

Regression analysis of multiplicative hazards model with time-dependent coefficient for sparse longitudinal covariates.

Journal of nonparametric statistics·2025
Same author

An Efficient Lasso Framework for Admixture-Aware Polygenic Scores.

bioRxiv : the preprint server for biology·2025
Same author

In vitro simulated digestion and fermentation characteristics of pumpkin polysaccharides and their effects on the human gut microbiota.

Food chemistry·2025
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Mar 25, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

496

Diagnosing scientific replicability through probabilistic distinguishability.

Peng Wang1,2, Hongyuan Cao2,3, Xiaoquan Wen4

  • 1School of Mathematics, Jilin University, Changchun, Jilin 130012, China.

Bioinformatics (Oxford, England)
|March 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a computational framework to quantify biological research irreplicability. The method identifies irreplicable instances and biological heterogeneity, enhancing research reproducibility.

More Related Videos

Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

991
Author Spotlight: Biological Standardization to Ensure Reproducibility and Harmonization in Research
04:50

Author Spotlight: Biological Standardization to Ensure Reproducibility and Harmonization in Research

Published on: August 4, 2023

1.7K

Related Experiment Videos

Last Updated: Mar 25, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

496
Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

991
Author Spotlight: Biological Standardization to Ensure Reproducibility and Harmonization in Research
04:50

Author Spotlight: Biological Standardization to Ensure Reproducibility and Harmonization in Research

Published on: August 4, 2023

1.7K

Area of Science:

  • Biological research
  • Computational biology
  • Genomics

Background:

  • Replicability is crucial in biological research, yet computational tools to assess it are lacking.
  • Existing methods struggle to quantify irreplicability and identify specific instances.
  • This study addresses the need for robust computational approaches to evaluate research reproducibility.

Purpose of the Study:

  • To develop an efficient and robust computational framework for quantifying and identifying irreplicable instances in biological research.
  • To establish a criterion for distinguishing replicable yet heterogeneous effects from noise.
  • To provide tools for detecting biases and uncovering biological heterogeneity.

Main Methods:

  • Introduced a distinguishability criterion to define acceptable heterogeneity in replicable studies.
  • Implemented a Bayesian model criticism approach with a Bayesian p-value for identifying irreplicable instances.
  • Developed an R package, DiscRep, for practical application of the framework.

Main Results:

  • Demonstrated the framework's efficacy in detecting batch effects in high-throughput experiments.
  • Successfully identified instances of publication bias using the proposed methods.
  • Applied the framework to GTEx eQTL data, revealing tissue-specific eQTLs and biological heterogeneity across tissues.

Conclusions:

  • The developed framework provides an efficient and robust method for assessing research replicability.
  • The approach can identify sources of irreproducibility, such as batch effects and publication bias.
  • The application to eQTL data highlights the framework's utility in uncovering complex biological heterogeneity.