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

729
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,...
729
Significance Testing: Overview01:04

Significance Testing: Overview

10.2K
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...
10.2K
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

27.9K
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...
27.9K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.0K
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...
6.0K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.4K
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...
4.4K
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

4.4K
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.
4.4K

You might also read

Related Articles

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

Sort by
Same author

Emotion matters: A call for systematic assessment of affective and motivational domains in clinical neuropsychology.

Journal of the International Neuropsychological Society : JINS·2026
Same author

Antibiotics for Prophylaxis of Infective Endocarditis in Pediatric Patients: Knowledge and Prescribing Practices Between Italian Dentists.

Antibiotics (Basel, Switzerland)·2026
Same author

The Uncertainty in Illness Questionnaire (UIQ): development and validation of a clinically oriented measure for patients and caregivers.

Health and quality of life outcomes·2026
Same author

The placebo effect in reading performance: A cross-over experimental study.

Acta psychologica·2026
Same author

Preliminary validation of an Italian version of the Athlete Burnout Questionnaire.

Frontiers in psychology·2026
Same author

Cognitive traits modulate the effects of images and familiarity on judgments of news accuracy.

Scientific reports·2026
Same journal

Adverse and positive childhood experiences in relation to adolescent mental health: sequential indirect associations.

Frontiers in psychology·2026
Same journal

Personality profiles and usage experience are associated with trust and dependence on generative AI: a latent profile analysis.

Frontiers in psychology·2026
Same journal

Editorial: Promoting replicability: empowering method and applied researchers in driving reliable results.

Frontiers in psychology·2026
Same journal

The mediating roles of the challenge appraisal in the relationship between the coach-athlete relationship and adolescent athletes' burnout.

Frontiers in psychology·2026
Same journal

Unpacking GenAI-enabled deep learning engagement: role perceptions, human-GenAI synergy strategies, and underlying mechanisms.

Frontiers in psychology·2026
Same journal

Violence exposure and cyberbullying among Chinese adolescents: the mediating role of moral disengagement.

Frontiers in psychology·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K

Null hypothesis significance testing vs. Bayesian inference using generalized linear mixed models with binary

Stefano Dalla Bona1, Andrea Spoto1, Marta Caserotti2

  • 1Department of General Psychology, Universita Degli Studi di Padova, Padova, Italy.

Frontiers in Psychology
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

Researchers explored Null Hypothesis Significance Testing (NHST) and Bayesian methods in psychology. The study highlights potential workflow and interpretation differences under realistic experimental conditions.

Keywords:
Bayes FactorBayesian analysisNHST analysismoral dilemmasunderpowered designs

More Related Videos

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

2.9K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

7.6K

Related Experiment Videos

Last Updated: May 5, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.8K
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

2.9K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

7.6K

Area of Science:

  • Psychology
  • Statistics
  • Data Analysis

Background:

  • Empirical research often involves uncertainty, with Null Hypothesis Significance Testing (NHST) being a common frequentist approach.
  • Researchers may shift to Bayesian methods when NHST yields non-significant results, risking conceptual confusion due to differing inferential questions.

Purpose of the Study:

  • To empirically examine the application of NHST and Bayesian methods to the same hypothesis test.
  • To compare workflow, performance, and interpretation of these methods under realistic experimental constraints, including underpowered sample sizes.

Main Methods:

  • Utilized Generalized Linear Mixed Models with a Binary Outcome.
  • Incorporated common experimental constraints into design-analysis planning to define a realistic sample size, assuming a 0.80 power threshold.

Main Results:

  • The study provides a real-world comparison of NHST and Bayesian approaches within a single hypothesis test.
  • Demonstrates potential differences in how these statistical frameworks handle data under realistic, constrained experimental conditions.

Conclusions:

  • NHST and Bayesian frameworks address distinct inferential questions and should not be used interchangeably.
  • Understanding their differences is crucial for accurate interpretation of research findings, especially in underpowered studies.