Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multiple Comparison Tests

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

Test for Homogeneity

Test for Homogeneity

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 be stated as...

Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

McNemar's Test

McNemar's Test

McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...

Introduction to Test of Independence

Introduction to Test of Independence

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:

Bonferroni Test

Bonferroni Test

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...

You might also read

Related Articles

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

Sort by

Same author

Bayesian Latent Class Models for Evaluating the Validity of Claim-based Definitions of Disease Outcomes.

Annals of clinical epidemiology·2024

Same author

Effects of 'SPRAT' programme for dietary and lifestyle education to improve psychosomatic symptoms and dietary habits among adolescents: a cluster randomised controlled trial.

BMC public health·2022

Same author

Spatial scan statistics can be dangerous.

Statistical methods in medical research·2021

Same author

Spatial clustering of suicide mortality and associated community characteristics in Kanagawa prefecture, Japan, 2011-2017.

BMC psychiatry·2020

Same author

Comparison of the Effectiveness of Lifestyle Modification with Other Treatments on the Incidence of Type 2 Diabetes in People at High Risk: A Network Meta-Analysis.

Nutrients·2019

Same author

Systematic review with network meta-analysis: indirect comparison of the efficacy of vonoprazan and proton-pump inhibitors for maintenance treatment of gastroesophageal reflux disease.

Journal of gastroenterology·2019

Search research articles

Related Experiment Video

Updated: Jul 15, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

A class of multiplicity adjusted tests for spatial clustering based on case-control point data.

Toshiro Tango¹

¹Department of Technology Assessment and Biostatistics, National Institute of Public Health, 3-6 Minami 2 chome, Wako, Saitama 351-0197, Japan. tango@niph.go.jp

|April 24, 2007

Summary

This study introduces improved statistical tests for spatial clustering of health events. The new methods offer a more accurate approximation and an integrated statistic for robust analysis of clustered data.

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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

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

Published on: February 15, 2017

Related Experiment Videos

Last Updated: Jul 15, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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

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

Published on: February 15, 2017

Area of Science:

Spatial statistics
Biostatistics
Epidemiology

Background:

Detecting spatial clustering of health events is crucial for public health.
Existing methods, like Cuzick and Edwards's test, rely on asymptotic normality, which can be inaccurate for moderate sample sizes.

Purpose of the Study:

To propose a class of quadratic form tests for spatial clustering using case-control point data.
To improve the approximation of the test statistic's asymptotic distribution.
To introduce an integrated test statistic for parameter optimization and multiple testing adjustment.

Main Methods:

Utilizing quadratic forms for spatial clustering detection in point data.
Comparing asymptotic normality approximation with a central chi-square distribution for accuracy.
Developing a minimum profile p-value statistic for parameter estimation and multiple testing.
Proposing a statistic to identify significant contributors to spatial clustering.

Main Results:

The central chi-square distribution provides a better approximation than asymptotic normality for the test statistic.
The proposed integrated test statistic effectively optimizes parameters and adjusts for multiple testing.
The methods successfully identified spatial clustering patterns in childhood leukemia and historical grave site data.

Conclusions:

The enhanced statistical tests offer a more accurate and robust approach to spatial clustering analysis.
The proposed methods are valuable tools for epidemiological and historical spatial data analysis.
Accurate spatial clustering detection aids in understanding disease patterns and historical site distribution.