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

Microsoft Excel: Pearson's Correlation01:18

Microsoft Excel: Pearson's Correlation

1.5K
Microsoft Excel is a powerful tool for statistical analysis, including calculating Pearson's correlation coefficient, which measures the strength and direction of a linear relationship between two continuous variables. Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, meaning as one variable increases, the other does too. A value close to -1 indicates a strong negative correlation, implying...
1.5K
Correlation and Regression00:53

Correlation and Regression

2.8K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
2.8K
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.2K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
1.2K
Coefficient of Correlation01:12

Coefficient of Correlation

7.6K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
7.6K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

7.2K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
7.2K
Correlations02:20

Correlations

35.4K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
35.4K

You might also read

Related Articles

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

Sort by
Same author

Engineered attenuated probiotic Escherichia coli oral vaccine provides effective protection against monkeypox virus by eliciting specific systemic and mucosal immunity.

Biomaterials·2026
Same author

Inhalable engineered probiotic outer membrane vesicles co-expressing multiple mpox antigens induce potent specific systemic and mucosal immune responses.

Materials today. Bio·2026
Same author

Tetrahedral DNA Nanostructure-Based Biomimetic Nanovesicles Attenuate Sepsis-Associated ARDS by Suppressing Glycolysis via the BMAL1/PFKFB3 Axis.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

A community-engaged investigation of residential polycyclic aromatic hydrocarbon exposures in West Eugene, OR.

Journal of exposure science & environmental epidemiology·2026
Same author

Telomere dysfunction is associated with exacerbated intermittent hypoxia-induced cognitive deficits and nerve damage.

Frontiers in aging neuroscience·2026
Same author

Spatial inheritance patterns across maize ears are associated with alleles that reduce pollen fitness.

The Plant journal : for cell and molecular biology·2026
Same journal

Bandwidth of gamma-distribution-shaped functions via Lambert W function.

Statistics & probability letters·2026
Same journal

Directional replicability: When can the factor of two be omitted.

Statistics & probability letters·2026
Same journal

Approximating win-loss probabilities based on the overall and event-free survival functions.

Statistics & probability letters·2025
Same journal

On exact Bayesian credible sets for discrete parameters.

Statistics & probability letters·2025
Same journal

On critical points of Gaussian random fields under diffeomorphic transformations.

Statistics & probability letters·2024
Same journal

Universally Consistent K-Sample Tests via Dependence Measures.

Statistics & probability letters·2024
See all related articles

Related Experiment Video

Updated: Nov 26, 2025

Three-dimensional Reconstruction of the Vascular Architecture of the Passive CLARITY-cleared Mouse Ovary
12:38

Three-dimensional Reconstruction of the Vascular Architecture of the Passive CLARITY-cleared Mouse Ovary

Published on: December 10, 2017

8.9K

Test-statistic correlation and data-row correlation.

Bin Zhuo1, Duo Jiang1, Yanming Di1

  • 1Department of Statistics, Oregon State University, Corvallis, OR, USA.

Statistics & Probability Letters
|December 11, 2020
PubMed
Summary
This summary is machine-generated.

Statistical tests applied to data matrices create correlations between test statistics when data rows are correlated. This study explores the link between these correlations and its implications.

Keywords:
bivariate normaltest-statistic correlationtwo-sample t-test

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.0K
Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
07:29

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

8.5K

Related Experiment Videos

Last Updated: Nov 26, 2025

Three-dimensional Reconstruction of the Vascular Architecture of the Passive CLARITY-cleared Mouse Ovary
12:38

Three-dimensional Reconstruction of the Vascular Architecture of the Passive CLARITY-cleared Mouse Ovary

Published on: December 10, 2017

8.9K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.0K
Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
07:29

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

8.5K

Area of Science:

  • Statistics
  • Data Analysis
  • Bioinformatics

Background:

  • Repeated statistical testing on data matrices can induce correlations among test statistics.
  • Understanding these induced correlations is crucial for accurate data interpretation.

Purpose of the Study:

  • To investigate the relationship between data-row correlation and test-statistic correlation.
  • To discuss the implications of this relationship in statistical analysis.

Main Methods:

  • Analysis of statistical tests applied to data matrices.
  • Mathematical investigation of correlation structures.

Main Results:

  • Demonstrated a direct link between data-row correlation and test-statistic correlation.
  • Identified how row correlations propagate to test statistic correlations.

Conclusions:

  • The correlation structure of the data directly influences the correlation of test statistics.
  • Awareness of this phenomenon is essential for avoiding misinterpretation in high-dimensional data analysis.