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

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...
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...
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

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 data...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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).
Significance Testing: Overview01:04

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...

You might also read

Related Articles

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

Sort by
Same author

PYCR1 induces ferroptosis via the PI3K/Akt signaling pathway to regulate the proliferation and migration of osteosarcoma.

Translational oncology·2026
Same author

Neural mechanisms of structural inference: An EEG investigation of linguistic phrase structure categorization.

Brain and language·2026
Same author

A High-Phosphogypsum Multi-Solid-Waste Cementitious Binder for Backfill: Cross-Scale Insight into Pore Structure and Strength Development.

Materials (Basel, Switzerland)·2026
Same author

Association Between Femoral Neck Bone Mineral Density and Osteoporotic Fracture Counts in U.S. Adults: A NHANES 2005-2020 Analysis.

Orthopedic reviews·2026
Same author

Impaired autophagy from TRPV4 activation drives α-synuclein pathology in a Parkinson's disease model: A toxicological insight.

Toxicology and applied pharmacology·2026
Same author

BIOMARKER DETECTION FOR DISEASE CLASSIFICATION IN LONGITUDINAL MICROBIOME DATA.

The annals of applied statistics·2026
Same journal

Simplifying debiased inference via automatic differentiation and probabilistic programming.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Principal stratification with U-statistics under principal ignorability.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Causal K-Means Clustering.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Inference of dependency knowledge graph for Electronic Health Records.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Correction to: Inference of dependency knowledge graph for Electronic Health Records.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Harmonized Estimation of Subgroup-Specific Treatment Effects in Randomized Trials: The Use of External Control Data.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Bayesian model selection using test statistics.

Jianhua Hu1, Valen E Johnson

  • 1University of Texas M. D. Anderson Cancer Center, Houston, USA.

Journal of the Royal Statistical Society. Series B, Statistical Methodology
|October 16, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian model selection method using classical test statistics, simplifying prior distribution requirements. This approach offers computational advantages and yields results comparable to existing Bayesian techniques.

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

Related Experiment Videos

Last Updated: Jun 19, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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

Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • Bayesian model selection traditionally necessitates specifying prior distributions for all model parameters.
  • This requirement poses practical limitations on the widespread application of Bayesian methods.

Purpose of the Study:

  • To propose a new Bayesian model selection approach that bypasses the need for complex prior distributions.
  • To leverage classical test statistics for computing Bayes factors, enhancing practicality and accessibility.

Main Methods:

  • The proposed method utilizes classical test statistics to calculate Bayes factors between competing models.
  • This technique avoids the intricate process of defining prior distributions for every parameter in each model.

Main Results:

  • The new approach demonstrated results similar to established Bayesian model selection and averaging techniques in test cases.
  • Significant computational and algorithmic benefits were observed compared to existing simulation-based methods.

Conclusions:

  • The developed method simplifies Bayesian model selection by eliminating complex prior specifications.
  • It offers practical advantages, including computational efficiency and ease of hyperparameter selection via prior-predictive simulation.