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

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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 ≠ 0.5.
Behrens&#8211;Fisher Test00:57

Behrens–Fisher Test

The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test is...
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Bonferroni Test01:10

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...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

You might also read

Related Articles

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

Sort by
Same author

Modelling non-linear personality change surrounding transitions: A review of statistical approaches.

European journal of personality·2026
Same author

Bayes factor hypothesis testing in meta-analyses: Practical advantages and methodological considerations.

Research synthesis methods·2026
Same author

Rigid control of motor unit firing rates in the human tibialis anterior muscle persists during neurofeedback.

Journal of neurophysiology·2026
Same author

To vary or not to vary: A flexible empirical Bayes factor for testing variance components.

The British journal of mathematical and statistical psychology·2026
Same author

Assessing mobile instant messenger networks with donated data.

Social network analysis and mining·2026
Same author

A tutorial on Bayesian hypothesis testing of correlation coefficients using the BFpack-module in JASP.

Behavior research methods·2025

Related Experiment Video

Updated: May 11, 2026

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

Bayes factors for testing inequality constrained hypotheses: Issues with prior specification.

Joris Mulder1

  • 1Department of Methodology and Statistics, Tilburg University, The Netherlands.

The British Journal of Mathematical and Statistical Psychology
|May 21, 2013
PubMed
Summary

New Bayesian methods improve testing of inequality constrained hypotheses by addressing parameter space complexity and information paradoxes. These approaches offer faster convergence to true hypotheses in simulations.

More Related Videos

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

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

Related Experiment Videos

Last Updated: May 11, 2026

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

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

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

Area of Science:

  • Statistics
  • Bayesian Inference
  • Hypothesis Testing

Background:

  • Testing inequality constrained hypotheses in Bayesian analysis presents challenges.
  • Existing methods may ignore parameter space complexity or lack invariance.
  • The information paradox can arise with certain prior specifications, like Zellner's g prior.

Purpose of the Study:

  • To identify and address issues in Bayesian hypothesis testing for inequality constraints.
  • To propose novel methods that overcome limitations of current approaches.
  • To enhance the reliability and efficiency of Bayesian hypothesis testing.

Main Methods:

  • Proposed partial Bayes factors using transformed minimal training samples.
  • Introduced a modified g prior approach by allowing g to approach infinity.
  • Utilized simulation studies to evaluate the performance of new methods.

Main Results:

  • New methods effectively address parameter space complexity and the information paradox.
  • Partial Bayes factors with transformed training samples yield centered posterior priors.
  • The proposed g prior approach demonstrated the fastest convergence to true inequality constrained hypotheses in simulations.

Conclusions:

  • The developed methods offer significant improvements for Bayesian inequality constrained hypothesis testing.
  • The modified g prior approach shows particular promise for rapid and accurate inference.
  • These advancements contribute to more robust statistical modeling in complex scenarios.