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

Sampling Plans

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...

Stratified Sampling Method

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...

You might also read

Related Articles

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

Sort by

Same author

Safety, Pharmacokinetics, and Pharmacodynamics of SHR6508, a Calcium-Sensing Receptor Agonist, in Maintenance Hemodialysis Patients With Secondary Hyperparathyroidism: A Multicenter, Randomized, Double-Blind, Placebo-Controlled Phase 1 Study.

Kidney medicine·2026

Same author

Mitochondrial-derived peptide MOTS-c activates metabolic signaling but blunts reparative function in human mesenchymal stromal cells.

Inflammation and regeneration·2026

Same author

Laparoscopic proximal gastrectomy with esophagogastrostomy using overlap method combined with gastric remnant "U"-shaped fold: a retrospective cohort study.

Frontiers in surgery·2026

Same author

Laser polishing for integrated enhancement of thin-walled LPBFed dental Co-Cr-Mo alloy frameworks.

Journal of the mechanical behavior of biomedical materials·2026

Same author

Association between CT-evaluated Poststenotic Dilatation in Human Renal Artery Stenosis and Kidney Release of MCP-1.

Radiology·2026

Same author

<i>Lyar</i> contributes to cell cycle progression and multi-lineage differentiation in mouse embryonic stem cells.

Frontiers in genetics·2026

Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026

Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026

Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026

Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026

Same journal

Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients.

Journal of biomedical informatics·2026

Same journal

DeepEN: A deep reinforcement learning framework for personalized enteral nutrition in critical care.

Journal of biomedical informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 15, 2025

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

ADSP: An adaptive sample pooling strategy for diagnostic testing.

Xuekui Zhang¹, Xiaolin Huang¹, Li Xing²

¹Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada.

Journal of Biomedical Informatics

|September 24, 2023

Summary

This summary is machine-generated.

This study introduces ADSP, a web-based application for adaptive sample pooling. It minimizes diagnostic tests needed, improving efficiency for large-scale screening during disease outbreaks or routine testing.

Keywords:

Adaptive strategy COVID-19 Diagnostic test Grouped test Sample pooling

More Related Videos

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Published on: July 30, 2020

Related Experiment Videos

Last Updated: Jul 15, 2025

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Published on: June 23, 2022

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Published on: July 30, 2020

Area of Science:

Computational Biology
Epidemiology
Bioinformatics

Background:

Large-scale diagnostic testing is crucial during disease outbreaks (e.g., COVID-19) and routine screenings.
Limited resources necessitate efficient sample pooling strategies to reduce testing time and costs.
Optimal sample splitting into test-pools is critical for maximizing diagnostic efficiency.

Purpose of the Study:

To develop an optimal adaptive strategy for splitting sample cohorts into test-pools.
To minimize the expected number of diagnostic tests required for a complete cohort analysis.

Main Methods:

Developed a novel algorithm that adaptively updates pool size based on real-time test results.
Implemented the adaptive sample pooling strategy into a web-based application, ADSP (https://ADSP.uvic.ca).
ADSP interactively guides users through the pooling process, incorporating feedback for dynamic strategy adjustment.

Main Results:

ADSP significantly reduces the number of tests required compared to other popular pooling methods in simulation studies.
The adaptive pooling strategy is robust to initial inaccuracies in disease prevalence estimates.
Demonstrated improved testing efficiency through dynamic pool size optimization.

Conclusions:

The ADSP web-based application provides an effective tool for researchers to optimize sample pooling for diagnostic tests.
This adaptive approach enhances overall testing efficiency, particularly for large-volume screening scenarios.
Facilitates resource and labor optimization in diagnostic testing.