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

Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

4.6K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
4.6K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

6.9K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
6.9K
Weighted Mean00:57

Weighted Mean

6.2K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
6.2K
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

5.4K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
5.4K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

8.0K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
8.0K
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

3.5K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Asyndromic surveillance of New York City emergency department diagnoses with the tree-temporal scan statistic.

Journal of public health research·2026
Same author

Innovative use of self-controlled methods for the evaluation of waning effectiveness of the COVID-19 monovalent third dose: comparison with a test-negative design.

Vaccine·2025
Same author

Applying prospective tree-temporal scan statistics to genomic surveillance data to detect emerging SARS-CoV-2 variants and salmonellosis clusters in New York City.

International journal of epidemiology·2025
Same author

Using Propensity Score Weighting to Enhance the Operating Characteristics of Power Prior in Leveraging External Data to Augment a Traditional Clinical Study.

Pharmaceutical statistics·2025
Same author

Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks.

JMIR public health and surveillance·2024
Same author

A Trial of Automated Outbreak Detection to Reduce Hospital Pathogen Spread.

NEJM evidence·2024

Related Experiment Video

Updated: Jan 4, 2026

Versatility of Protocols for Resistance Training and Assessment Using Static and Dynamic Ladders in Animal Models
08:31

Versatility of Protocols for Resistance Training and Assessment Using Static and Dynamic Ladders in Animal Models

Published on: December 17, 2021

3.2K

Tango's maximized excess events test with different weights.

Changhong Song1, Martin Kulldorff

  • 1Department of Statistics, University of Connecticut, Storrs, CT 06269, USA. changhon@stat.uconn.edu

International Journal of Health Geographics
|December 17, 2005
PubMed
Summary
This summary is machine-generated.

Tango's maximized excess events test (MEET) effectively detects global disease clustering. The study found that while the original weight function performs well, other options also offer good statistical power for detecting spatial disease patterns.

More Related Videos

The TreadWheel: Interval Training Protocol for Gently Induced Exercise in Drosophila melanogaster
07:21

The TreadWheel: Interval Training Protocol for Gently Induced Exercise in Drosophila melanogaster

Published on: June 8, 2018

12.1K
Community-based Adapted Tango Dancing for Individuals with Parkinson's Disease and Older Adults
09:19

Community-based Adapted Tango Dancing for Individuals with Parkinson's Disease and Older Adults

Published on: December 9, 2014

26.2K

Related Experiment Videos

Last Updated: Jan 4, 2026

Versatility of Protocols for Resistance Training and Assessment Using Static and Dynamic Ladders in Animal Models
08:31

Versatility of Protocols for Resistance Training and Assessment Using Static and Dynamic Ladders in Animal Models

Published on: December 17, 2021

3.2K
The TreadWheel: Interval Training Protocol for Gently Induced Exercise in Drosophila melanogaster
07:21

The TreadWheel: Interval Training Protocol for Gently Induced Exercise in Drosophila melanogaster

Published on: June 8, 2018

12.1K
Community-based Adapted Tango Dancing for Individuals with Parkinson's Disease and Older Adults
09:19

Community-based Adapted Tango Dancing for Individuals with Parkinson's Disease and Older Adults

Published on: December 9, 2014

26.2K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Spatial Analysis

Background:

  • Tango's maximized excess events test (MEET) demonstrates high statistical power for detecting global disease clustering.
  • The test adeptly handles multiple testing across various spatial scales, enhancing its ability to identify diverse clustering processes.
  • The test's performance is influenced by the choice of weight function, necessitating an evaluation of its sensitivity and optimal selection for different clustering scenarios.

Purpose of the Study:

  • To evaluate the performance of Tango's maximized excess events test (MEET) across a spectrum of weight functions.
  • To determine the sensitivity of the MEET to different weight function choices.
  • To identify weight functions that provide optimal statistical power for detecting various spatial disease clustering patterns.

Main Methods:

  • The study involved an evaluation of Tango's maximized excess events test (MEET).
  • Performance was assessed across a wide range of functional forms for the weight function.
  • Statistical power was analyzed in relation to different weight function choices.

Main Results:

  • The statistical power of the MEET significantly varied depending on the selected weight function.
  • Tango's original weight function demonstrated robust performance.
  • Several alternative weight functions were identified as providing substantial statistical power.

Conclusions:

  • Tango's maximized excess events test (MEET) is recommended for global disease clustering detection.
  • The test can be effectively utilized with either its original weight function or alternative high-power weight functions.
  • The choice of weight function impacts the test's power, but effective options are available.