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

Test for Homogeneity01:23

Test for Homogeneity

2.0K
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...
2.0K
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

26.4K
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...
26.4K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

131
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
131
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.0K
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

3.6K
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)...
3.6K

You might also read

Related Articles

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

Sort by
Same author

Bias of Odds Ratio Estimate in Fisher's Exact Test.

International journal of methods in psychiatric research·2026
Same author

Bias correction for Cohen's <i>d</i>.

The Journal of general psychology·2023
Same author

Bootstrap Estimate of Bias for Intraclass Correlation.

Journal of applied measurement·2020
Same author

A Note on the Relation between Item Difficulty and Discrimination Index.

Journal of applied measurement·2019
Same author

Common language effect size for correlations.

The Journal of general psychology·2019
Same author

Sample Size and the Precision of the Confidence Interval in Meta-analyses.

Therapeutic innovation & regulatory science·2018
Same journal

Using photo elicitation in focused ethnography with adolescents: methodological lessons from an urban greenspace study.

Nurse researcher·2026
Same journal

Adapting interpretative phenomenological analysis: a multilayered perspectival design for health research.

Nurse researcher·2026
Same journal

An art-based mixed-methods study exploring children's environmental preferences in home-based nursing research.

Nurse researcher·2026
Same journal

Using fuzzy-set qualitative comparative analysis in clinical nursing research.

Nurse researcher·2026
Same journal

Integrating the experience sampling method and intersectionality to capture lived realities in nursing research.

Nurse researcher·2026
Same journal

Fraudulent qualitative study participants: experiences of two nurse researchers.

Nurse researcher·2026
See all related articles

Related Experiment Video

Updated: Jul 4, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.6K

The permutation test: a simple way to test hypotheses.

Xiaofeng Steven Liu1

  • 1Department of Educational and Developmental Science, University of South Carolina, Columbia, SC, US.

Nurse Researcher
|January 30, 2024
PubMed
Summary
This summary is machine-generated.

Permutation tests offer a simpler, more robust alternative for null hypothesis significance testing compared to traditional methods like the t-test. They avoid complex distribution theory and strict model assumptions, making hypothesis testing more accessible for researchers.

Keywords:
data analysisquantitative researchresearch

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

18.4K
Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies
06:27

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies

Published on: January 10, 2025

738

Related Experiment Videos

Last Updated: Jul 4, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.6K
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

18.4K
Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies
06:27

Establishment of Rat Models Mimicking Gender-affirming Hormone Therapies

Published on: January 10, 2025

738

Area of Science:

  • Statistics
  • Quantitative Research Methods

Background:

  • Null hypothesis significance testing often relies on complex distribution theory.
  • Traditional tests like the t-test have strict model assumptions.
  • Permutation tests provide an accessible alternative without complex statistical theory.

Purpose of the Study:

  • To introduce and explain permutation tests.
  • To demonstrate their application using examples of independent and dependent t-tests.
  • To provide practical implementation guidance for researchers.

Main Methods:

  • Historical overview of permutation tests.
  • Explanation of their underlying principles.
  • Implementation examples using R programming language for generating null distributions and P-values.

Main Results:

  • Permutation tests yield conclusions comparable to t-tests.
  • They are easier to understand and implement than traditional parametric tests.
  • R code is provided for practical application.

Conclusions:

  • Permutation tests are a valuable tool for hypothesis testing.
  • They offer robust alternatives to t-tests by not requiring strict model assumptions.
  • Researchers can readily incorporate permutation tests into their statistical analyses.