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

Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

2.3K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
2.3K
Bonferroni Test01:10

Bonferroni Test

2.9K
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...
2.9K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

336
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
336
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

2.2K
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...
2.2K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

241
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,...
241
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

4.7K
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.
4.7K

You might also read

Related Articles

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

Sort by
Same author

Representing objects and features in long-term memory: A case for direct feature-feature binding.

Journal of experimental psychology. Learning, memory, and cognition·2026
Same author

Assessing qualitative individual differences with Bayesian hierarchical latent-mixture models.

Psychological methods·2026
Same author

Multinomial models of the repetition-based truth effect: Investigating the role of prior knowledge.

Memory & cognition·2026
Same author

Extensions of multinomial processing tree models for continuous variables: A simulation study comparing parametric and non-parametric approaches.

Behavior research methods·2025
Same author

The Interval Consensus Model: Aggregating Continuous Bounded Interval Responses.

Psychometrika·2025
Same author

Modeling the link between the plausibility of statements and the truth effect.

Psychonomic bulletin & review·2025
Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
Same journal

Best practices in multilevel modeling for within-cluster group comparisons: An evaluation of coding strategies reflecting group composition and heterogeneity.

Psychological methods·2026
Same journal

A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model.

Psychological methods·2026
See all related articles

Related Experiment Video

Updated: Sep 27, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K

Waldian t tests: Sequential Bayesian t tests with controlled error probabilities.

Martin Schnuerch1, Daniel W Heck2, Edgar Erdfelder1

  • 1Department of Psychology, School of Social Sciences, University of Mannheim.

Psychological Methods
|April 14, 2022
PubMed
Summary
This summary is machine-generated.

Bayesian t tests offer advantages but lack error control. The new Waldian t test combines Bayesian methods with sequential analysis to control error probabilities, providing a robust alternative for researchers.

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

3.4K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.2K

Related Experiment Videos

Last Updated: Sep 27, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K
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

3.4K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.2K

Area of Science:

  • Psychological research methodology
  • Statistical inference
  • Bayesian statistics

Background:

  • Bayesian t tests are gaining traction in psychology as an alternative to null-hypothesis significance testing (NHST).
  • While Bayesian t tests allow quantifying evidence for the null hypothesis and optional stopping, they suffer from uncontrolled error probabilities.
  • Existing methods to control errors in Bayesian t tests necessitate complex, time-consuming simulations.

Purpose of the Study:

  • To introduce a novel sequential procedure, the Waldian t test, that integrates Bayesian t tests with Abraham Wald's sequential probability ratio test.
  • To demonstrate how the Waldian t test controls expected frequentist error probabilities while retaining Bayesian advantages.
  • To provide a user-friendly web application for implementing the Waldian t test.

Main Methods:

  • Proposed a sequential probability ratio test combining Bayesian t tests with decision criteria from Abraham Wald's work.
  • Applied the Waldian t test framework to three recent Bayesian t test specifications.
  • The procedure assumes a distribution for the effect size under the alternative hypothesis.

Main Results:

  • Waldian t tests control expected frequentist error probabilities (Type I and Type II) under specified statistical models.
  • Nominal error rates serve as upper bounds for actual expected error rates.
  • The proposed method is justifiable from both Bayesian and frequentist perspectives.

Conclusions:

  • Waldian t tests offer a statistically rigorous approach by controlling error probabilities, addressing a key limitation of standard Bayesian t tests.
  • This method bridges Bayesian and frequentist statistical paradigms, enhancing the reliability of research findings.
  • The availability of a web application facilitates the adoption of this advanced statistical procedure by researchers.