Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Quantifying and Rejecting Outliers: The Grubbs Test

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...

Detection of Gross Error: The Q Test

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...

Hypothesis: Accept or Fail to Reject?

Hypothesis: Accept or Fail to Reject?

The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null...

You might also read

Related Articles

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

Sort by

Same author

Fine-tuning and structured prompting strategies for question answering over full-text biomedical research articles.

PloS one·2026

Same author

Effect of ancestry and shared genetic architecture of serious mental illness on symptoms and cognition in an admixed Latin American population.

Research square·2026

Same author

PerturbPlan: An analytical framework for designing Perturb-seq experiments.

bioRxiv : the preprint server for biology·2026

Same author

Effect of ancestry and shared genetic architecture of serious mental illness on symptoms and cognition in an admixed Latin American population.

medRxiv : the preprint server for health sciences·2026

Same author

Association Between High-Risk Preexisting Substance Use and Increased Postoperative Opioid Dose Use Among Adolescents Undergoing Ambulatory Orthopedic Surgery.

Paediatric anaesthesia·2026

Same author

GWAS-informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral density.

Genome biology·2025

Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026

Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026

Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026

Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026

Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026

Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 26, 2025

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

Filtering the rejection set while preserving false discovery rate control.

Eugene Katsevich¹, Chiara Sabatti², Marina Bogomolov³

¹Department of Statistics, University of Pennsylvania.

Journal of the American Statistical Association

|June 22, 2023

Summary

This summary is machine-generated.

We introduce Focused BH, a method to adjust multiple testing procedures when hypotheses have structures like ICD or GO. This ensures reliable results even after filtering, maintaining false discovery rate control.

Keywords:

Gene Ontology enrichment analysis directed acyclic graph outer nodes phenome-wide association study structured multiple testing tree

More Related Videos

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Published on: June 17, 2022

Related Experiment Videos

Last Updated: Jul 26, 2025

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Published on: June 17, 2022

Area of Science:

Statistical methodology
Bioinformatics
Genomics

Background:

Hypothesis structures (e.g., ICD, GO) create redundancies in multiple testing.
Filtering hypothesis rejections can invalidate statistical guarantees.
Interpretability is hindered by redundant rejections.

Purpose of the Study:

To propose Focused BH, a principled method for adjusting multiple testing after applying pre-specified filters.
To ensure false discovery rate control when using domain-specific hypothesis structures.
To provide a flexible methodology applicable to various filtering scenarios.

Main Methods:

Developed the Focused BH methodology to incorporate pre-specified filters.
Provided theoretical proofs for false discovery rate control under specific conditions.
Conducted simulations to evaluate performance across diverse settings.

Main Results:

Focused BH maintains false discovery rate control, even with monotonic filters and positively dependent p-values.
Simulations demonstrate strong performance of Focused BH in various scenarios.
The method proved practical in real-world analyses using ICD and GO data.

Conclusions:

Focused BH offers a reliable approach to multiple testing with structured hypotheses and filtering.
This methodology enhances the interpretability of results without compromising statistical validity.
Focused BH is applicable to diverse scientific domains with structured data.