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

Cluster Sampling Method01:20

Cluster Sampling Method

13.7K
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...
13.7K
Sampling Distribution01:12

Sampling Distribution

16.2K
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...
16.2K
Sampling Methods: Overview01:06

Sampling Methods: Overview

1.1K
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...
1.1K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

12.5K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
12.5K
Sampling Theorem01:15

Sampling Theorem

1.1K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.1K
Outliers and Influential Points01:08

Outliers and Influential Points

5.4K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
5.4K

You might also read

Related Articles

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

Sort by
Same author

VizGenie: Toward Self-Refining, Domain-Aware Workflows for Next-Generation Scientific Visualization.

IEEE transactions on visualization and computer graphics·2025
Same author

Correction: Polyamine biosynthesis and eIF5A hypusination are modulated by the DNA tumor virus KSHV and promote KSHV viral infection.

PLoS pathogens·2025
Same author

Clearing the path: Unraveling bisphenol a removal and degradation mechanisms for a cleaner future.

Journal of environmental management·2024
Same author

Colorimetric detection of Cr(VI) in water using tetramethyl benzidine (TMB) as an indicator.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2024
Same author

Environmental bisphenol A disrupts methylation of steroidogenic genes in the ovary of Paradise threadfin Polynemus paradiseus via abnormal DNA methylation: Implications for human exposure and health risk assessment.

Chemosphere·2024
Same author

Cr-Detector: A simple chemosensing system for onsite Cr (VI) detection in water.

PloS one·2024
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 27, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.1K

Multivariate Pointwise Information-Driven Data Sampling and Visualization.

Soumya Dutta1, Ayan Biswas1, James Ahrens1

  • 1Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

Scientists developed a new data sub-sampling method to reduce large scientific datasets. This technique preserves multi-variable relationships, enabling accurate analysis of complex scientific events and queries.

Keywords:
data reductioninformation theorymultivariate samplingpointwise mutual information (PMI)query-driven visualizationspecific correlationstatistical distributionstotal correlation

More Related Videos

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

10.4K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K

Related Experiment Videos

Last Updated: Nov 27, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.1K
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

10.4K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.8K

Area of Science:

  • Data Science
  • Scientific Computing
  • Computational Science

Background:

  • Modern supercomputers generate massive scientific simulation data.
  • Analyzing large-scale multivariate spatiotemporal data requires effective summarization.
  • Domain experts need to understand multi-variable relationships for complex event analysis.

Purpose of the Study:

  • To develop a data summarization technique for large scientific datasets.
  • To preserve important data properties and multi-variable relationships during data reduction.
  • To enable accurate domain-specific queries on reduced datasets.

Main Methods:

  • Proposed a data sub-sampling algorithm for statistical data summarization.
  • Leveraged pointwise information theoretic measures to quantify multi-variable statistical association.
  • Generated sub-sampled data preserving statistical associations among multiple variables.

Main Results:

  • The proposed algorithm effectively reduces large-scale scientific data.
  • Sub-sampled data accurately preserves multi-variable statistical associations.
  • Multivariate feature queries and analysis are performed effectively on reduced data.

Conclusions:

  • The developed multivariate association-driven sampling algorithm is effective for scientific data summarization.
  • This method enables efficient analysis of complex scientific events using reduced datasets.
  • The approach facilitates accurate domain-specific queries on large-scale scientific data.