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

Sample Size Calculation

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...

You might also read

Related Articles

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

Sort by

Same author

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026

Same author

Youth Perspectives on the Climate Crisis: Motivation and Action Pathways.

Developmental science·2026

Same author

Neural variability across the transition to motherhood: Enhanced moment-to-moment neural variability during mentalizing in first-time mothers.

Journal of neuroendocrinology·2026

Same author

Dimensional Associations Between Conduct Problems and Brain Structure Across 18 International Cohorts in ENIGMA.

Journal of the American Academy of Child and Adolescent Psychiatry·2026

Same author

Mood trajectories showing resilience and recovery in young people during and after the COVID-19 pandemic.

Scientific reports·2026

Same author

Adolescents' emotional support to friends and family: A resiliency-promoting factor amidst and beyond the COVID-19 pandemic.

Developmental psychology·2025

Same journal

Neonatal brain-age models in full- and preterm infants.

Developmental cognitive neuroscience·2026

Same journal

Neural representations of the value of helping the family during adolescence.

Developmental cognitive neuroscience·2026

Same journal

Parental speech and gesture scaffolding moderate the neural dynamics of parent-child synchrony.

Developmental cognitive neuroscience·2026

Same journal

Evaluating the reliability of functional near-infrared spectroscopy data in the context of a reasoning paradigm.

Developmental cognitive neuroscience·2026

Same journal

Contextualizing the adolescent social brain: Links to social health using data from the Adolescent Brain Cognitive Development Study.

Developmental cognitive neuroscience·2026

Same journal

Untangling relationships between cognitive development and child and adolescent mental health: Findings from the ABCD Study.

Developmental cognitive neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 12, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Sample size estimation for task-related functional MRI studies using Bayesian updating.

Eduard T Klapwijk¹, Joran Jongerling², Herbert Hoijtink³

¹Erasmus University Rotterdam, Netherlands.

Developmental Cognitive Neuroscience

|December 25, 2024

Summary

This summary is machine-generated.

Determining sample size for functional MRI (fMRI) studies is challenging. This study introduces empirical Bayesian updating for sample size estimation, improving research planning and reliability for fMRI data.

Keywords:

Bayesian updating Effect size Power analysis R package Region of interest Sample sizes

More Related Videos

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

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

Related Experiment Videos

Last Updated: May 12, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

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

Area of Science:

Neuroscience
Cognitive Neuroscience
Neuroimaging

Background:

Functional magnetic resonance imaging (fMRI) studies require adequate sample sizes for reliable effect detection.
Traditional sample size calculations using published effect sizes are often difficult for fMRI research.
Accurate sample size estimation is crucial for the validity and reproducibility of fMRI findings.

Purpose of the Study:

To present an alternative method for sample size estimation in task-related fMRI studies using empirical Bayesian updating.
To provide researchers with a practical approach for planning research projects and reporting sample size estimations.
To enable the refinement of sample size estimates as new data become available.

Main Methods:

Utilized empirical Bayesian updating to estimate sample sizes based on existing similar fMRI data.
Employed four existing fMRI datasets to illustrate the method.
Assessed Cohen's d for hemodynamic response and Pearson correlation for task effect and age as a covariate.

Main Results:

Demonstrated that required sample sizes vary significantly across different tasks and regions of interest in fMRI.
Showcased the utility of Bayesian updating for refining sample size estimations with new data.
Validated the proposed method using real-world fMRI datasets.

Conclusions:

Empirical Bayesian updating offers a robust alternative for sample size estimation in task-related fMRI.
The method enhances the planning and execution of fMRI studies by providing data-driven sample size recommendations.
An R package is provided to facilitate the application of this Bayesian updating approach in future fMRI research.