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

Sample Size Calculation01:19

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...
One-Way ANOVA: Unequal Sample Sizes01:15

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:
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...

You might also read

Related Articles

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

Sort by
Same author

Three steps toward dose optimization for oncology dose finding.

Contemporary clinical trials communications·2024
Same author

A gated group sequential design for seamless Phase II/III trial with subpopulation selection.

BMC medical research methodology·2023
Same author

A hybrid design for dose-finding oncology clinical trials.

International journal of cancer·2022
Same author

Mixture survival trees for cancer risk classification.

Lifetime data analysis·2022
Same author

Rejoinder to Letter to the Editor "The Hazards of Period Specific and Weighted Hazard Ratios".

Statistics in biopharmaceutical research·2021
Same author

Inferring latent heterogeneity using many feature variables supervised by survival outcome.

Statistics in medicine·2021
Same journal

Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control.

Pharmaceutical statistics·2026
Same journal

Harmonic Fowlkes-Mallows Index for Medical Diagnostics Tests and Optimal Cut-Off Point Selection of Binary Diseases.

Pharmaceutical statistics·2026
Same journal

Early Phase Dose-Finding Designs for CAR-T Cell Therapies.

Pharmaceutical statistics·2026
Same journal

Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints.

Pharmaceutical statistics·2026
Same journal

CUI-MET: A Clinical Utility Index Based Analysis and Decision Framework for Dose Optimization in Multiple-Dose, Multiple-Outcome Randomized Trials.

Pharmaceutical statistics·2026
Same journal

Will the Pharmaceutical Industry Need Statisticians in an AI World?

Pharmaceutical statistics·2026
See all related articles

Related Experiment Video

Updated: Jun 22, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Sample size calculation for an agreement study.

Jason J Z Liao1

  • 1Merck Research Laboratories, West Point, PA 19486, USA.

Pharmaceutical Statistics
|June 10, 2009
PubMed
Summary
This summary is machine-generated.

This study proposes a new sample size calculation method for agreement studies, crucial for comparing measurement methods in science. The approach uses discordance and tolerance rates to determine adequate sample sizes for reliable study design.

More Related Videos

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Related Experiment Videos

Last Updated: Jun 22, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Area of Science:

  • Biostatistics
  • Experimental Design
  • Medical Research Methodology

Background:

  • Comparing measurement methods is essential in experimental sciences and medicine.
  • Existing research on assessing measurement agreement lacks focus on sample size determination.
  • Designing agreement studies requires robust methods for sample size calculation.

Purpose of the Study:

  • To propose a novel method for calculating sample size in agreement studies.
  • To address the gap in sample size determination for studies comparing measurement methods.
  • To provide a practical approach for researchers designing agreement studies.

Main Methods:

  • A sample size calculation method based on the interval approach for concordance.
  • Defining concordance based on a pre-specified number of discordances (k) for a given sample size (n).
  • Utilizing discordance rate and tolerance probability for sample size determination.

Main Results:

  • The proposed method offers a structured way to calculate sample size for agreement studies.
  • Demonstration of the approach using a real-world dataset.
  • The method facilitates quantifying agreement studies based on defined rates.

Conclusions:

  • The interval approach for concordance provides a viable method for sample size calculation in agreement studies.
  • This method enhances the design and reliability of studies comparing measurement techniques.
  • Researchers can effectively determine appropriate sample sizes using discordance and tolerance probabilities.