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

Sampling Plans01:23

Sampling Plans

244
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...
244
Cluster Sampling Method01:20

Cluster Sampling Method

12.5K
Appropriate sampling methods ensure 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 cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.5K
Stratified Sampling Method01:16

Stratified Sampling Method

12.6K
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...
12.6K
Systematic Sampling Method01:17

Systematic Sampling Method

10.8K
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. Data are the result of sampling from a 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.
Systematic sampling is one of the simplest methods...
10.8K
Sampling Methods: Overview01:06

Sampling Methods: Overview

482
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
482
Review and Preview01:13

Review and Preview

9.2K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
9.2K

You might also read

Related Articles

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

Sort by
Same author

A unified closed-loop stabilization framework for fixed-wing UAVs via natural-selection-enhanced multi-objective particle swarm optimization.

Scientific reports·2026
Same author

Nitrogen-Mediated Orbital-Compatible π-Extension: Balancing Excited-State Components and Suppressing Vibrational Broadening Toward Redshifted Narrowband MR-TADF Emitters.

Angewandte Chemie (International ed. in English)·2026
Same author

Sevoflurane-Associated Plasma Extracellular Vesicles Promote Aggressive Phenotypes in Cervical Cancer Cells with Concurrent DG Remodeling and EGFR/PKCα/NF-κB Activation.

Biomedicines·2026
Same author

E-Spray-Deposited CsPbBr<sub>3</sub>/Cs<sub>2</sub>AgBiBr<sub>6</sub> Type-II Heterojunction Enabling High-Sensitivity Self-Powered X-ray Detection.

ACS applied materials & interfaces·2026
Same author

Legume proteins with a focus on seed fractions: Unraveling structural foundations for health-promoting applications.

Food chemistry·2026
Same author

Bipyridine-Based One-Dimensional Perovskites with Type-II Band Alignment Enables High-Sensitivity Direct X-ray Detection.

ACS applied materials & interfaces·2026

Related Experiment Video

Updated: Aug 31, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K

Hierarchical Sampling for the Visualization of Large Scale-Free Graphs.

Bo Jiao, Xin Lu, Jingbo Xia

    IEEE Transactions on Visualization and Computer Graphics
    |August 25, 2022
    PubMed
    Summary

    This study introduces hierarchical structure sampling (HSS) to compress large graphs for visualization. The HSS algorithm effectively preserves core community structures, important minority features, and peripheral connections in scale-free graphs.

    More Related Videos

    Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
    06:01

    Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

    Published on: December 12, 2019

    8.6K
    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    5.0K

    Related Experiment Videos

    Last Updated: Aug 31, 2025

    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
    05:12

    ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

    Published on: January 16, 2019

    11.5K
    Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
    06:01

    Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

    Published on: December 12, 2019

    8.6K
    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    5.0K

    Area of Science:

    • Computer Science
    • Data Visualization
    • Network Analysis

    Background:

    • Graph sampling is crucial for visualizing large networks within limited screen space.
    • Existing methods may not adequately preserve critical structural features of scale-free graphs.

    Purpose of the Study:

    • To propose a novel hierarchical structure model for scale-free graph partitioning.
    • To develop an algorithm that preserves key characteristics of these partitions during sampling.
    • To evaluate the algorithm's effectiveness for graph visualization.

    Main Methods:

    • Partitioning scale-free graphs into core, vertical, and periphery blocks.
    • Designing the hierarchical structure sampling (HSS) algorithm to maintain block-specific properties.
    • Analyzing global statistical properties and local visual features for evaluation.

    Main Results:

    • The HSS algorithm successfully replicates connections in the core, preserves node/degree distributions in the vertical graph, and proportionally samples the periphery.
    • Evaluation using global and local features confirms the algorithm's efficacy.
    • The method is applicable to scale-free graphs ranging from hundreds to one million nodes.

    Conclusions:

    • The proposed hierarchical structure model and HSS algorithm offer an effective approach for sampling scale-free graphs.
    • The method enhances the visualization of large-scale networks by preserving essential structural information.
    • HSS provides a valuable tool for visual analysis of complex graph data.