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

Contingency Table01:29

Contingency Table

A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
Confidence Coefficient01:24

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...
Introduction to Test of Independence01:21

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:
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...

You might also read

Related Articles

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

Sort by
Same author

Comment on Selvin et al. The Glucose Management Indicator: Time to Change Course? Diabetes Care 2024;47:906-914.

Diabetes care·2024
Same author

Testing conditional independence in sets of I × J tables by means of moment and correlation score tests with application to HPV vaccine.

Statistics in medicine·2016
See all related articles

Related Experiment Video

Updated: Jul 2, 2026

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

Score and profile likelihood confidence intervals for contingency table parameters.

Joseph B Lang1

  • 1Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA 52245, USA. joseph-lang@uiowa.edu

Statistics in Medicine
|August 23, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a simple computational method for calculating confidence intervals for contingency tables. The new

More Related Videos

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing
15:00

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

Published on: February 7, 2025

Related Experiment Videos

Last Updated: Jul 2, 2026

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

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing
15:00

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

Published on: February 7, 2025

Area of Science:

  • Statistics
  • Computational Statistics
  • Data Analysis

Background:

  • Existing methods for calculating confidence intervals for contingency table parameters have limitations.
  • Current approaches are often case-specific or applicable to a narrow range of parameters.

Purpose of the Study:

  • To present a general and computationally simple algorithm for score and profile likelihood confidence intervals.
  • To address limitations of existing methods in terms of parameter applicability and specificity.

Main Methods:

  • Development of the 'sliding quadratic' computational algorithm.
  • Application of the algorithm to novel examples in contingency table analysis.
  • Comparison with existing interval calculation methods.

Main Results:

  • The 'sliding quadratic' algorithm offers a broader applicability to various parameters.
  • The method is general and not case-specific, simplifying computation.
  • Simulation studies indicate score and profile likelihood intervals outperform Wald intervals.

Conclusions:

  • The proposed 'sliding quadratic' method provides a versatile and straightforward approach to confidence interval computation.
  • This method enhances the analysis of contingency table parameters, offering advantages over traditional techniques.