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 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...

Identifying Statistically Significant Differences: The F-Test

Identifying Statistically Significant Differences: The F-Test

The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...

Accuracy and Errors in Hypothesis Testing

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...

Bonferroni Test

Bonferroni Test

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...

One-Way ANOVA: Unequal Sample Sizes

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

Errors In Hypothesis Tests

Errors In Hypothesis Tests

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.

You might also read

Related Articles

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

Sort by

Same author

Pragmatic Designs for Combining Cyclic Laboratory and Monitor Data.

Statistics in medicine·2026

Same author

Investigation on the Cytotoxicity of Hydroxycinnamic and Hydroxybenzoic Acid-Based Natural Antioxidant Conjugated Terpyridine Analogues toward Triple-Negative Breast Cancer.

ACS medicinal chemistry letters·2026

Same author

Fiducial Confidence Intervals for Agreement Measures Among Raters Under a Generalized Linear Mixed Effects Model.

Statistics in medicine·2026

Same author

Co-expression of superoxide and peroxide scavenging antioxidant enzymes rescues β cells from hypoxia and hyperglycemia-induced oxidative damage.

Tissue & cell·2026

Same author

DYNAMIC RISK PREDICTION FOR CERVICAL PRECANCER SCREENING WITH CONTINUOUS AND BINARY LONGITUDINAL BIOMARKERS.

The annals of applied statistics·2025

Same author

Role of microalgae in reducing antibiotic-resistant bacteria in synthetic wastewater.

Environmental monitoring and assessment·2025

Search research articles

Related Experiment Video

Updated: May 31, 2026

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

Hypothesis testing, power and sample size determination for between group comparisons in fMRI experiments.

Dulal K Bhaumik¹, Anindya Roy, Nicole A Lazar

¹Center for Health Statistics, University of Illinois at Chicago, 1601 W Taylor Street (MC 912), Chicago, IL 60612, United States.

Statistical Methodology

|July 15, 2011

Summary

This summary is machine-generated.

This study introduces a new statistical test for brain imaging studies. It helps determine the required sample size for accurate group comparisons, crucial for costly neuroimaging research.

More Related Videos

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression

Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression

Published on: May 19, 2015

Related Experiment Videos

Last Updated: May 31, 2026

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression

Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression

Published on: May 19, 2015

Area of Science:

Neuroimaging
Statistical analysis
Brain imaging techniques

Background:

Functional magnetic resonance imaging (fMRI) presents complex statistical challenges.
Accurate statistical methods are vital for interpreting neuroimaging data.
Subject costs in neuroimaging necessitate efficient study design.

Purpose of the Study:

To develop a large-sample statistical test for between-group comparisons in neuroimaging.
To establish a method for calculating necessary sample sizes to achieve desired statistical power.
To address critical statistical needs in modern neuroscience research.

Main Methods:

Development of a large-sample statistical test for group comparisons.
Utilizing simulation studies to determine sample size requirements.
Evaluating test performance under various alternative hypotheses and significance levels.

Main Results:

A novel statistical test for between-group comparisons was developed.
Sample size calculations were performed to achieve target power.
The methodology provides a framework for optimizing neuroimaging study designs.

Conclusions:

The developed statistical test and sample size calculation method are essential for neuroscientists.
This approach enhances the rigor and efficiency of brain imaging research.
Addressing statistical challenges improves the reliability of findings in functional magnetic resonance imaging studies.