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

Introduction to the Sign Test01:10

Introduction to the Sign Test

816
The sign test is an important tool in nonparametric statistics, offering a straightforward yet effective method for analyzing matched pairs, nominal data, or hypotheses concerning the median of a population. It transforms data points into positive or negative signs, avoiding the need for assumptions about data distribution and instead focusing on the direction of change. It is particularly valuable when data does not conform to the normal distribution requirements of many parametric tests. For...
816
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

131
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
131
Sign Test for Nominal Data01:12

Sign Test for Nominal Data

95
The sign test is a nonparametric method used to evaluate hypotheses about the median of a single sample or to compare the medians of two related samples. The sign test is particularly useful when dealing with nominal data, which includes distinct categories without an inherent order, such as names, labels, and preferences. Nominal data restricts statistical analysis to evaluating population proportions rather than mean or median values that require continuous data.
For example, consider a...
95
Sign Test for Median of Single Population01:20

Sign Test for Median of Single Population

114
In general, the sign test serves as a nonparametric method to test hypotheses about the median of a single population when the data does not follow a known distribution. This simplicity makes it particularly useful for small sample sizes or when the assumptions of parametric tests cannot be met. The process begins with identifying a null hypothesis, typically stating that the population median equals a specific value. The alternative hypothesis could be that the median is either not equal to,...
114
Significance Testing: Overview01:04

Significance Testing: Overview

3.4K
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...
3.4K
Bonferroni Test01:10

Bonferroni Test

2.7K
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...
2.7K

You might also read

Related Articles

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

Sort by
Same author

Music Ensemble: a large dataset on musicianship, cognition, and personality in musicians and nonmusicians.

Scientific data·2026
Same author

Concerns About Theorizing, Relevance, Generalizability, and Methodology Across Two Crises in Social Psychology.

International review of social psychology·2025
Same author

Concerns About Replicability Across Two Crises in Social Psychology.

International review of social psychology·2025
Same author

Association of Chrono-Nutritional Profiles with Weight Loss and Comorbidity Remission After Bariatric Surgery in Patients with Severe Obesity.

Nutrients·2025
Same author

A systemic approach to better coordination in science.

Nature human behaviour·2025
Same author

Exploratory research in sport and exercise science: Perceptions, challenges, and recommendations.

Journal of sports sciences·2025
Same journal

Testing linear hypotheses in repeated measures generalized linear models using external information.

Psychometrika·2026
Same journal

When Do Unifactorial Items Increase the Reliability?

Psychometrika·2026
Same journal

Longitudinal Designs for Diagnostic Models: Identification and Estimation.

Psychometrika·2026
Same journal

Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model.

Psychometrika·2026
Same journal

Revelle's Beta: The Wait Is Over-Computation Becomes Possible.

Psychometrika·2026
Same journal

On dimensional implication graphs.

Psychometrika·2026
See all related articles

Related Experiment Video

Updated: Jun 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

9.9K

Post-selection Inference in Multiverse Analysis (PIMA): An Inferential Framework Based on the Sign Flipping Score

Paolo Girardi1, Anna Vesely2, Daniël Lakens3

  • 1Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30172, Venezia-Mestre, VE, Italy. paolo.girardi@unive.it.

Psychometrika
|April 25, 2024
PubMed
Summary
This summary is machine-generated.

Researchers can now use a Post-selection Inference approach to Multiverse Analysis (PIMA) to rigorously test hypotheses across many data analysis choices. This method addresses the replication crisis by providing a robust inferential procedure for complex models.

Keywords:
flipping scoremultiverse analysisreplicabilityreproducibilitystatistical inferencetesting

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

Related Experiment Videos

Last Updated: Jun 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

9.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

Area of Science:

  • Statistics
  • Data Science
  • Research Methodology

Background:

  • Data analysis involves numerous choices, some arbitrary, contributing to the replication crisis.
  • Multiverse analysis evaluates result stability across choices but lacks inferential power.
  • Specification curve analysis offers inference but is limited to linear models and simple hypothesis testing.

Purpose of the Study:

  • To introduce a flexible and general inferential approach for multiverse analysis.
  • To enable hypothesis testing across a wide range of data specifications and generalized linear models.
  • To provide strong control of the family-wise error rate for robust statistical claims.

Main Methods:

  • Development of a Post-selection Inference approach to Multiverse Analysis (PIMA).
  • Utilizing a conditional resampling procedure for inference.
  • Formal proof of Type I error rate control and statistical power computation via simulation.

Main Results:

  • PIMA offers a flexible and general inferential framework for multiverse analysis.
  • The approach controls the family-wise error rate, allowing claims of null hypothesis rejection for significant specifications.
  • Simulation studies confirm the controlled Type I error rate and assess statistical power.

Conclusions:

  • PIMA provides a powerful inferential tool for navigating the multiverse of data analyses.
  • The method enhances the reliability of research findings by accounting for analytical choices.
  • Practical recommendations are provided for implementing PIMA in real-world data analysis, including a COVID-19 vaccine hesitancy case study.