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

Fisher's Exact Test01:08

Fisher's Exact Test

Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of the...
Factorial Design02:01

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
F Distribution01:19

F Distribution

The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...
Two-Way ANOVA01:17

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
Behrens–Fisher Test00:57

Behrens–Fisher Test

The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test is...
What is an ANOVA?01:16

What is an ANOVA?

The Analysis of Variance or ANOVA is a statistical test developed by Ronald Fisher in 1918. It is performed on three or more samples to check for equality between their means.
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples should be randomly and...

You might also read

Related Articles

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

Sort by
Same author

2021 Canadian Surgery Forum: Virtual, online Sept. 21-24, 2021.

Canadian journal of surgery. Journal canadien de chirurgie·2022
Same author

Ecological drivers of helminth infection patterns in the Virunga Massif mountain gorilla population.

International journal for parasitology. Parasites and wildlife·2022
Same author

Author Correction: Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity.

Nature communications·2021
Same author

Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity.

Nature communications·2020
Same author

DEMONSTRATION OF THE POTENTIAL AND DIFFICULTIES OF COMBINED TL AND OSL MEASUREMENTS OF TLD-600 AND TLD-700 FOR THE DETERMINATION OF THE DOSE COMPONENTS IN COMPLEX NEUTRON-GAMMA RADIATION FIELDS.

Radiation protection dosimetry·2020
Same author

DOSE DEPENDENCE OF RADIATION INDUCED DAMAGE IN THE THERMOLUMINESCENT RESPONSE OF LIF:Mg,Ti (TLD-100).

Radiation protection dosimetry·2020
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: May 29, 2026

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

Fisher discriminant analysis and factor analysis.

G D Riccia1, A Shapiro

  • 1Ben-Gurion University of the Negev, Beer-Sheva, Israel; Istituto di Matematica, Universita di Udine, 33100 Udine, Italy.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

Factor analysis provides equivalent information to Fisher discriminant analysis for multidimensional data under specific conditions. This suggests using factor analysis when Fisher discriminant analysis is not suitable, like in clustering tasks.

More Related Videos

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

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Related Experiment Videos

Last Updated: May 29, 2026

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

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

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Area of Science:

  • Multivariate statistics
  • Data analysis

Background:

  • Factor analysis and Fisher discriminant analysis are common techniques for analyzing multidimensional data.
  • Understanding the relationship between these methods can reveal new analytical possibilities.

Purpose of the Study:

  • To demonstrate the equivalence between factor analysis and Fisher discriminant analysis under specific conditions.
  • To highlight the applicability of factor analysis in scenarios where Fisher discriminant analysis is not suitable.

Main Methods:

  • Comparative analysis of information derived from factor analysis and Fisher discriminant analysis.
  • Examination of conditions under which the two methods yield equivalent results.

Main Results:

  • Information from factor analysis is equivalent to Fisher discriminant analysis when standard factor analysis model conditions are met.
  • Factor analysis can be effectively used in clustering problems, a domain where Fisher discriminant analysis is typically not applied.

Conclusions:

  • Factor analysis offers a viable alternative to Fisher discriminant analysis, particularly in specific data structures and analytical goals.
  • The findings support the broader application of factor analysis in statistical modeling and data exploration.