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

Introduction to Test of Independence01:21

Introduction to Test of Independence

3.1K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
3.1K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.9K
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:
6.9K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.4K
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...
4.4K
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
Fisher's Exact Test01:08

Fisher's Exact Test

1.4K
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...
1.4K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

572
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
572

You might also read

Related Articles

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

Sort by
Same author

Testing Multivariate Effect Sizes in Multiple-Endpoint Studies.

Multivariate behavioral research·2016
See all related articles

Related Experiment Video

Updated: Mar 26, 2026

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

19.1K

Simultaneous Inference Using Finite Intersection Tests: A Better Mousetrap.

N H Timm

    Multivariate Behavioral Research
    |January 21, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the finite intersection test (FIT) procedure for analyzing mean differences in behavioral and social sciences. FIT offers a powerful alternative to classical simultaneous test procedures (STP) for both univariate and multivariate designs.

    More Related Videos

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
    07:12

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

    Published on: July 1, 2014

    12.8K
    Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
    08:19

    Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

    Published on: October 20, 2023

    1.9K

    Related Experiment Videos

    Last Updated: Mar 26, 2026

    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

    19.1K
    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
    07:12

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

    Published on: July 1, 2014

    12.8K
    Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
    08:19

    Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

    Published on: October 20, 2023

    1.9K

    Area of Science:

    • Behavioral and Social Sciences
    • Statistics
    • Experimental Design

    Background:

    • Classical simultaneous test procedures (STP) are widely used in behavioral and social sciences.
    • Familiar methods include those developed by Duncan, Scheffe, Tukey, and Roy and Bose.
    • These methods are applied to analyze differences among means.

    Purpose of the Study:

    • Introduce the finite intersection test (FIT) procedure to applied researchers.
    • Compare FIT with classical simultaneous test procedures (STP).
    • Illustrate the application of FIT for analyzing mean differences in experimental designs.

    Main Methods:

    • The study focuses on the finite intersection test (FIT) procedure.
    • Comparison is made between FIT and classical simultaneous test procedures (STP).
    • Illustrative examples cover both univariate and multivariate experimental designs.

    Main Results:

    • The finite intersection test (FIT) procedure is presented as an alternative to classical methods.
    • FIT is shown to be applicable for analyzing differences among all means.
    • The procedure is demonstrated for both univariate and multivariate data.

    Conclusions:

    • The finite intersection test (FIT) procedure is a valuable tool for applied researchers.
    • FIT provides a method for analyzing mean differences in complex experimental designs.
    • Researchers are encouraged to consider FIT alongside established simultaneous test procedures (STP).