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

Statistical Significance01:50

Statistical Significance

23.1K
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...
23.1K
Significance Testing: Overview01:04

Significance Testing: Overview

12.9K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
12.9K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.3K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.8K
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:
6.8K
Kruskal-Wallis Test01:19

Kruskal-Wallis Test

1.4K
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...
1.4K
Bonferroni Test01:10

Bonferroni Test

3.5K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images-Nevus and Melanoma.

Cancers·2024
Same author

DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues.

Nature communications·2024
Same author

Mono- and Dimeric Sorbicillinoid Inhibitors Targeting IL-6 and IL-1β from the Mangrove-Derived Fungus <i>Trichoderma reesei</i> BGRg-3.

International journal of molecular sciences·2023
Same author

Genetic Diversity and Signatures of Selection in the Roughskin Sculpin (<i>Trachidermus fasciatus</i>) Revealed by Whole Genome Sequencing.

Biology·2023
Same author

Patient sex, racial and ethnic disparities in emergency department triage: A multi-site retrospective study.

The American journal of emergency medicine·2023
Same author

High-dimensional immune profiling using mass cytometry reveals IL-17A-producing γδ T cells as biomarkers in patients with T-cell-activated idiopathic severe aplastic anemia.

International immunopharmacology·2023

Related Experiment Video

Updated: Mar 8, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Statistical significance for hierarchical clustering.

Patrick K Kimes1, Yufeng Liu1,2,3,4,5, David Neil Hayes5

  • 1Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, North Carolina, U.S.A.

Biometrics
|January 19, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Monte Carlo method for statistically validating clusters identified through hierarchical clustering. This approach helps distinguish true biological patterns from random variations in high-dimensional data.

Keywords:
High-dimensionHypothesis testingMultiple correctionUnsupervised learning

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.9K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

839

Related Experiment Videos

Last Updated: Mar 8, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.9K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

839

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Statistical Learning

Background:

  • Cluster analysis is vital for unsupervised exploration of high-dimensional datasets.
  • Hierarchical clustering is widely used in genomics for its multi-layered structure discovery.
  • Distinguishing true clusters from sampling artifacts in hierarchical clustering remains a challenge.

Purpose of the Study:

  • To develop a statistically rigorous method for assessing the significance of clusters in hierarchical clustering.
  • To address the complexities of nested structures and multiple testing inherent in hierarchical clustering analysis.
  • To provide a reliable approach for validating discovered clustering structures in biological data.

Main Methods:

  • A Monte Carlo-based approach for statistical significance testing in hierarchical clustering.
  • Implementation as a sequential testing procedure to control the family-wise error rate.
  • Validation through simulation studies and application to cancer gene expression datasets.

Main Results:

  • The proposed method provides theoretical justification for its statistical validity.
  • Simulation studies demonstrate the approach's power in detecting genuine clustering structures.
  • Successful application to real-world cancer gene expression data showcases its practical utility.

Conclusions:

  • The Monte Carlo sequential testing procedure offers a robust solution for significance testing in hierarchical clustering.
  • This method enhances the reliability of cluster analysis in high-dimensional biological data.
  • It aids in differentiating meaningful biological patterns from statistical noise.