Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

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,...

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

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...

Expected Frequencies in Goodness-of-Fit Tests

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: Overview

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...

Statistical Hypothesis Testing

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...

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...

You might also read

Related Articles

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

Sort by

Same author

Effect Size Rules of Thumb for One-Dimensional Functional Data With an Application to Gait Analysis.

Statistics in medicine·2026

Same author

Multidisciplinary management and molecular characterization of inferior vena cava leiomyosarcoma: a case report.

World journal of surgical oncology·2025

Same author

Confidence sets for intraclass correlation coefficients in test-retest curve measurements.

Journal of biomechanics·2024

Same author

Portal hypertension-like pattern in coronavirus disease 2019 acute respiratory distress syndrome.

Journal of critical care·2024

Same author

Limb salvage and survival after urgent surgical treatment of popliteal artery aneurysm.

World journal of emergency surgery : WJES·2023

Same author

Machine perfusion techniques for liver transplantation - A meta-analysis of the first seven randomized-controlled trials.

Journal of hepatology·2023

Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026

Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026

Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026

Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026

Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026

Same journal

A Bayesian phase I/II platform design with data augmentation accounting for delayed outcomes.

Biometrics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 26, 2026

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

The interval testing procedure: A general framework for inference in functional data analysis.

Alessia Pini¹, Simone Vantini²

¹MOX-Department of Mathematics, Politecnico di Milano, Via Bonardi 9, 20133 Milano, Italy. alessia.pini@polimi.it.

|January 27, 2016

Summary

This summary is machine-generated.

We introduce the Interval Testing Procedure (ITP) for functional data analysis. This novel method controls errors and tests hypotheses, offering a new approach for functional data inference.

Keywords:

Family wise error rate Functional data analysis Inference Multiple comparison Permutation method

More Related Videos

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

Related Experiment Videos

Last Updated: Mar 26, 2026

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

Area of Science:

Statistics
Functional Data Analysis

Background:

Functional data analysis requires specialized inferential techniques.
Existing methods may not adequately address the complexities of functional hypotheses.

Purpose of the Study:

Introduce the Interval Testing Procedure (ITP) for functional data.
Develop a novel inferential technique for testing functional hypotheses.
Implement interval-wise control of the family-wise error rate for functional data.

Main Methods:

Represent functional data using a functional basis.
Test sets of consecutive basis coefficients.
Compute adjusted p-values using a novel strategy.
Define and implement interval-wise family-wise error rate control.

Main Results:

ITP provides interval-wise control of the family-wise error rate.
Simulation studies demonstrate ITP's performance compared to other procedures.
ITP was applied to analyze hemodynamical features in cerebral aneurysm pathology.

Conclusions:

ITP is a novel and effective inferential technique for functional data.
The method offers a robust approach to hypothesis testing in functional data analysis.
ITP is available in the fdatest R package for practical application.