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...

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.

What are Estimates?

What are Estimates?

It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates.
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean,...

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...

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...

Testing a Claim about Population Proportion

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

You might also read

Related Articles

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

Sort by

Same author

Balsa Wood-Loaded Polyvinyl Alcohol/Chitosan/Zinc Gluconate Hydrogel Applied as Wound Dressing.

Polymers·2026

Same author

Targeted Delivery of Schisandrin A to Cardiac Endothelium Alleviates Myocardial Ischemia-Reperfusion Injury via Suppression of Ferroptosis.

International journal of nanomedicine·2026

Same author

Disrupting fumarylacetoacetate hydrolase by stratified nanoplatforms orchestrates metabolic-immune reprogramming and prevents post-ablation HCC relapse.

Nature communications·2026

Same author

An LC-MS-guided AI enabled serum molecule-interpretable SERS platform for exploratory metabolomic analysis in Alzheimer's disease.

Analytica chimica acta·2026

Same author

Ferroptosis-related proteins orchestrate aortic dissection: unveiling novel molecular signatures and therapeutic avenues.

Scientific reports·2026

Same author

Neuromodulation and Copper Chelation Reverse Sleep Fragmentation-Aggravated Myocardial Ischemia-Reperfusion Injury by Targeting NET-Induced Endothelial Cuproptosis.

Research (Washington, D.C.)·2026

Search research articles

Related Experiment Video

Updated: May 23, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Estimation of sample size and testing power (part 7).

Liang-ping Hu¹, Xiao-lei Bao, Xue Guan

¹Consulting Center of Biomedical Statistics, Academy of Military Medical Sciences, Beijing 100850, China. lphu812@sina.com

Zhong Xi Yi Jie He Xue Bao = Journal of Chinese Integrative Medicine

|April 17, 2012

Summary

This study details sample size estimation and power testing for two-factor factorial designs. It covers quantitative data analysis for experiments with two simultaneous, independent factors.

More Related Videos

Randomized Controlled Trial to Study the Acute Effects of Strength Exercise on Insulin Sensitivity in Obese Adults

Randomized Controlled Trial to Study the Acute Effects of Strength Exercise on Insulin Sensitivity in Obese Adults

Published on: December 1, 2023

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Related Experiment Videos

Last Updated: May 23, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Randomized Controlled Trial to Study the Acute Effects of Strength Exercise on Insulin Sensitivity in Obese Adults

Randomized Controlled Trial to Study the Acute Effects of Strength Exercise on Insulin Sensitivity in Obese Adults

Published on: December 1, 2023

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Area of Science:

Statistics
Experimental Design

Background:

Factorial designs are crucial in research for examining multiple independent variables simultaneously.
A two-factor factorial design involves two experimental factors, with groups equaling the product of factor levels.
This design treats factors equally, without primary/secondary distinctions or nesting.

Purpose of the Study:

To introduce methods for estimating sample size in two-factor factorial designs.
To explain power testing for quantitative data within this design framework.
To provide a guide for researchers utilizing two-factor factorial designs.

Main Methods:

Focuses on statistical approaches for sample size calculation.
Explains power analysis techniques applicable to quantitative outcomes.
Assumes random assignment of subjects to experimental groups.

Main Results:

Provides a framework for determining adequate sample sizes.
Enables the assessment of statistical power for detecting effects.
Facilitates robust analysis of quantitative data in factorial studies.

Conclusions:

Accurate sample size and power estimation are vital for valid two-factor factorial designs.
The methods discussed enhance the reliability of quantitative research findings.
Researchers can confidently apply these techniques for experimental planning and analysis.