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 Methods: Overview01:06

Sampling Methods: Overview

3.7K
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...
3.7K
Cluster Sampling Method01:20

Cluster Sampling Method

15.3K
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...
15.3K
Statgraphics01:10

Statgraphics

448
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
448
Sampling Plans01:23

Sampling Plans

1.1K
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...
1.1K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.5K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
3.5K
Sampling Distribution01:12

Sampling Distribution

18.7K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
18.7K

You might also read

Related Articles

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

Sort by
Same author

Multi-View Biomedical Foundation Models for Molecule-Target and Property Prediction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

An Autoethnography on Visualization Literacy: A Wicked Measurement Problem.

IEEE transactions on visualization and computer graphics·2025
Same author

A Critical Analysis of the Usage of Dimensionality Reduction in Four Domains.

IEEE transactions on visualization and computer graphics·2025
Same author

PromptAid: Visual Prompt Exploration, Perturbation, Testing and Iteration for Large Language Models.

IEEE transactions on visualization and computer graphics·2025
Same author

MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models.

Proceedings of the ... Conference on Fairness, Accountability, and Transparency·2025
Same author

Mechanistic modeling of social conditions in disease-prediction simulations via copulas and probabilistic graphical models: HIV case study.

Health care management science·2024
Same journal

Graph Pattern Matching based reassembly - 3DGPM.

IEEE computer graphics and applications·2026
Same journal

Making Learning Visible: Turning Public Engagement into Evidence for Academic Learning.

IEEE computer graphics and applications·2026
Same journal

LlymX: Multimodal LLM-Augmented XR for Context-Aware Information Access.

IEEE computer graphics and applications·2026
Same journal

Dynamic Gaussian-Based Digital Twin Reconstruction of Articulated Multi-Joint Objects.

IEEE computer graphics and applications·2026
Same journal

Steiner and Poisson Traversal Initializations: Initial Curve Optimization for Geometric Flow-based Surface Filling.

IEEE computer graphics and applications·2026
Same journal

Insight Into the Insight Toolkit.

IEEE computer graphics and applications·2026
See all related articles

Related Experiment Video

Updated: Mar 8, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.5K

Sampling for Scalable Visual Analytics.

Bum Chul Kwon, Janu Verma, Peter J Haas

    IEEE Computer Graphics and Applications
    |January 20, 2017
    PubMed
    Summary
    This summary is machine-generated.

    Sampling is crucial for scalable visual analysis. This work explores improving sampling techniques for broader user interaction and wider adoption in visual analytics.

    More Related Videos

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.9K
    Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
    09:17

    Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

    Published on: September 13, 2022

    2.8K

    Related Experiment Videos

    Last Updated: Mar 8, 2026

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
    10:58

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

    Published on: January 2, 2011

    10.5K
    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.9K
    Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
    09:17

    Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

    Published on: September 13, 2022

    2.8K

    Area of Science:

    • Data Science
    • Computer Science
    • Human-Computer Interaction

    Background:

    • Interactive visual analysis relies on scalable techniques.
    • Database sampling methods exist for aggregation queries.
    • Understanding user interaction with sampling is limited.

    Purpose of the Study:

    • Improve existing database sampling methods for visualization.
    • Extend sampling techniques to a wider range of visual analytics applications.
    • Investigate user interaction with sampling to enhance adoption.

    Main Methods:

    • Literature review of database sampling for visualization.
    • Conceptual extension of sampling techniques.
    • Analysis of user interaction patterns with sampling tools.

    Main Results:

    • Identified limitations in current sampling for visualization.
    • Proposed extensions for broader applicability.
    • Highlighted the importance of user interaction in sampling adoption.

    Conclusions:

    • Sampling is vital for scalable visual analytics.
    • Further research can enhance sampling methods and user experience.
    • Broader adoption requires understanding and improving user interaction.