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

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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 from...
Kruskal-Wallis Test01:19

Kruskal-Wallis Test

The Kruskal-Wallis test, also known as the Kruskal-Wallis H test, serves as a nonparametric alternative to the one-way ANOVA, offering a solution for analyzing the differences across three or more independent groups based on a single, ordinal-dependent variable. This statistical test is particularly valuable in scenarios where the data does not meet the normal distribution assumption required by its parametric counterparts. Kruskal-Wallis test is designed typically to handle ordinal data or...
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...

You might also read

Related Articles

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

Sort by
Same author

Vibration-Induced Texture Deterioration in Kiwifruit: Molecular Mechanisms and Modulation by 1-MCP-Mediated Pectin Stabilization.

Journal of agricultural and food chemistry·2026
Same author

Urbanization, wildfire exposure, and youth mental health: a narrative review.

Current opinion in psychiatry·2026
Same author

Quantifying the evidence on associated factors for diabetes-related foot complications: An umbrella review of published systematic reviews and meta-analyses.

Diabetes research and clinical practice·2026
Same author

Synergistic Nitrogen Removal and Community Interaction Mechanism of Immobilized Bacteria Algae Symbiosis System.

Molecules (Basel, Switzerland)·2026
Same author

Comparison of procedural efficiency and safety between transradial and transfemoral approaches in elective diagnostic cerebral angiography: A retrospective cohort study.

Medicine·2026
Same author

A reduction-sensitive lipophilic dihydroartemisinin prodrug in a self-microemulsifying drug delivery system for treating breast cancer lung metastasis via intestinal lymphatic transport.

International journal of pharmaceutics: X·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
Same journal

Addressing the influence of unmeasured confounding in observational studies with time-to-event outcomes: a semiparametric sensitivity analysis approach.

Biostatistics (Oxford, England)·2026
Same journal

IV-learner: learning conditional average treatment effects using instrumental variables.

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

Related Experiment Video

Updated: May 31, 2026

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

High-dimensional test for one-sided hypotheses.

Rongrong Wang1, Shrabanti Chowdhury2, Hanwen Huang3

  • 1Center for Biostatistics and Qualitative Methodology, University of Pittsburgh, Pittsburgh, PA 15213, United States.

Biostatistics (Oxford, England)
|May 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces the Sum Max-Component (SMC) test, a novel method for one-sided high-dimensional mean vector testing. The SMC test demonstrates effectiveness in analyzing complex datasets and has applications in gene set enrichment analysis.

Keywords:
enrichment analysishigh-dimensional datahigh-grade serous ovarian cancermultivariate one-sided test

More Related Videos

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

An Olfactory Preference Test for Measuring Olfactory Hedonic Biases in Mouse Models of Depression
06:27

An Olfactory Preference Test for Measuring Olfactory Hedonic Biases in Mouse Models of Depression

Published on: July 11, 2025

Related Experiment Videos

Last Updated: May 31, 2026

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

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

An Olfactory Preference Test for Measuring Olfactory Hedonic Biases in Mouse Models of Depression
06:27

An Olfactory Preference Test for Measuring Olfactory Hedonic Biases in Mouse Models of Depression

Published on: July 11, 2025

Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics

Background:

  • High-dimensional data analysis is crucial across scientific domains.
  • Existing methods for high-dimensional mean vector testing are often two-sided.
  • One-sided tests are needed for detecting directional changes, such as gene up-regulation or down-regulation.

Purpose of the Study:

  • To develop and validate a new method for one-sided high-dimensional mean vector testing.
  • To address the gap in existing statistical tools for directional analysis in high-dimensional data.
  • To apply the new method to a relevant biological problem.

Main Methods:

  • Introduction of the Sum Max-Component (SMC) test.
  • Asymptotic behavior analysis of the SMC test statistic.
  • Extensive validation in finite sample scenarios.
  • Application to gene set enrichment analysis using proteomic data.

Main Results:

  • The SMC test shows effective performance in finite samples.
  • The test achieves competitive rates for type I error and statistical power.
  • The method was successfully applied to analyze ovarian cancer proteomic data.

Conclusions:

  • The SMC test is a valuable new tool for one-sided high-dimensional mean vector testing.
  • The method shows promise for applications in bioinformatics and genomics, particularly in enrichment analysis.
  • The study highlights the potential of the SMC test in understanding complex diseases like ovarian cancer.