Jove
Visualize
Contact Us

Related Concept Videos

Odds Ratio01:09

Odds Ratio

1.2K
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
1.2K
Ratio Level of Measurement00:54

Ratio Level of Measurement

20.1K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
20.1K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

5.7K
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).
5.7K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

457
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
457
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

5.5K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
5.5K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

6.9K
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...
6.9K

You might also read

Related Articles

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

Sort by
Same author

Technical note: a functional data analysis approach to analyze the light-adapted electroretinogram in children and adolescents.

Documenta ophthalmologica. Advances in ophthalmology·2026
Same author

A Sequential Design for Extreme Quantile Estimation Under Binary Sampling.

Entropy (Basel, Switzerland)·2026
Same author

Some Theoretical Foundations of Bare-Simulation Optimization of Some Directed Distances between Fuzzy Sets Respectively Basic Belief Assignments.

Entropy (Basel, Switzerland)·2024
Same author

Testing exchangeability of multivariate distributions.

Journal of applied statistics·2023
Same author

Interpoint distance tests for high-dimensional comparison studies.

Journal of applied statistics·2022
Same author

Minimum Divergence Estimators, Maximum Likelihood and the Generalized Bootstrap.

Entropy (Basel, Switzerland)·2021
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles
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 Experiment Video

Updated: Nov 27, 2025

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

921

Likelihood Ratio Testing under Measurement Errors.

Michel Broniatowski1, Jana Jurečková2,3, Jan Kalina4

  • 1Faculté de Mathématiques, Laboratoire de Probabilité, Statistique et Modélisation, Université Pierre et Marie Curie (Sorbonne Université), 4 place Jussieu, 75252 Paris CEDEX 05, France.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study addresses statistical hypothesis testing with measurement errors. Ignoring these errors alters type I error rates, but a novel minimax test, using pseudo-capacities, offers robust performance against misspecified hypotheses.

Keywords:
2-alternating capacitiesmeasurement errorsmisspecified hypothesis and alternativerobust testingtwo-sample test

More Related Videos

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.1K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.5K

Related Experiment Videos

Last Updated: Nov 27, 2025

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

921
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.1K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.5K

Area of Science:

  • Statistics
  • Hypothesis Testing
  • Measurement Error Analysis

Background:

  • Likelihood ratio tests are fundamental in statistical inference.
  • Measurement errors in observed data can significantly distort hypothesis testing outcomes.
  • Ignoring measurement errors can lead to deviations in type I error rates from nominal values.

Purpose of the Study:

  • To analyze the impact of measurement errors on the likelihood ratio test.
  • To develop a robust statistical test for situations with unobservable measurement errors.
  • To derive a minimax test for misspecified hypotheses in the presence of measurement errors.

Main Methods:

  • Investigated the properties of the likelihood ratio test when observations are subject to additive measurement errors (Z_i = X_i + δV_i).
  • Derived a minimax test by leveraging the concept of pseudo-capacities.
  • Employed numerical experiments to evaluate the performance of the proposed test.

Main Results:

  • The standard likelihood ratio test, when ignoring measurement errors, maintains its power property but with altered type I error rates.
  • A novel minimax test was successfully derived for families of misspecified hypotheses and alternatives.
  • Numerical experiments demonstrated the practical applicability and effectiveness of the developed test.

Conclusions:

  • Measurement errors necessitate adjustments in standard hypothesis testing procedures.
  • The proposed minimax test offers a robust solution for statistical inference under measurement error conditions.
  • The study highlights the utility of pseudo-capacities in constructing reliable statistical tests.