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: Sample Types01:18

Sampling Methods: Sample Types

3.8K
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...
3.8K
Sampling Methods: Overview01:06

Sampling Methods: Overview

4.1K
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...
4.1K
Stratified Sampling Method01:16

Stratified Sampling Method

16.5K
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...
16.5K
Sampling Distribution01:12

Sampling Distribution

20.1K
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...
20.1K
Sampling Theorem01:15

Sampling Theorem

1.6K
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.
1.6K
Random Sampling Method01:09

Random Sampling Method

15.9K
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. Among the various sampling methods used by...
15.9K

You might also read

Related Articles

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

Sort by
Same author

Revisiting Pierre Gy's formula (TOS) - A return to size-density classes for applications to contaminated soils, coated particular aggregates and mixed material systems.

Analytica chimica acta·2022
Same author

Sampling of particulate materials with significant spatial heterogeneity - Theoretical modification of grouping and segregation factors involved with correct sampling errors: Fundamental Sampling Error and Grouping and Segregation Error.

Analytica chimica acta·2019
Same author

Transition to circular economy requires reliable statistical quantification and control of uncertainty and variability in waste.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA·2016
Same author

Empirical Approach for Estimating Reference Material Heterogeneity and Sample Minimum Test Portion Mass for "Nuggety" Precious Metals (Au, Pd, Ir, Pt, Ru).

Analytical chemistry·2016
Same author

Adequacy and verifiability of pharmaceutical mixtures and dose units by variographic analysis (Theory of Sampling) - A call for a regulatory paradigm shift.

International journal of pharmaceutics·2015
Same author

Representative sampling for food and feed materials: a critical need for food/feed safety.

Journal of AOAC International·2015

Related Experiment Video

Updated: Apr 15, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

36.2K

Materials properties: heterogeneity and appropriate sampling modes.

Kim H Esbensen

    Journal of AOAC International
    |March 26, 2015
    PubMed
    Summary

    Understanding material heterogeneity is crucial for accurate sampling in food and feed sectors. This paper explains sampling errors and how the Theory of Sampling mitigates them.

    Area of Science:

    • Food Science
    • Analytical Chemistry
    • Sampling Theory

    Background:

    • Effective decision-making relies on accurate analytical results, which are preceded by primary sampling.
    • Heterogeneous materials present significant challenges in sampling, potentially leading to errors.
    • The Theory of Sampling provides a framework for managing these challenges.

    Purpose of the Study:

    • To elucidate the phenomenon and concepts of material heterogeneity in sampling.
    • To describe the adverse effects of heterogeneity on sampling processes.
    • To provide strategies for understanding, describing, and managing sampling errors caused by heterogeneity.

    Main Methods:

    • Discussion of sampling errors arising from material heterogeneity.
    • Explanation of the principles of the Theory of Sampling.

    More Related Videos

    Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
    08:16

    Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

    Published on: March 13, 2014

    19.6K
    Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils
    09:16

    Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

    Published on: November 25, 2016

    17.6K

    Related Experiment Videos

    Last Updated: Apr 15, 2026

    Sampling Soils in a Heterogeneous Research Plot
    07:11

    Sampling Soils in a Heterogeneous Research Plot

    Published on: January 7, 2019

    36.2K
    Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
    08:16

    Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

    Published on: March 13, 2014

    19.6K
    Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils
    09:16

    Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

    Published on: November 25, 2016

    17.6K
  • Conceptual analysis of managing heterogeneity in sampling processes.
  • Main Results:

    • Identification of up to five types of sampling errors related to heterogeneous materials.
    • Distinction between errors originating from the sampling target's heterogeneity and those from the sampling process itself.
    • Emphasis on the necessity of understanding and managing heterogeneity to ensure reliable analytical outcomes.

    Conclusions:

    • Competent understanding of heterogeneous material characteristics is essential for all stakeholders in the food and feed sectors.
    • Proper sampling, guided by the Theory of Sampling, is critical to counteract heterogeneity effects.
    • Effective management of sampling errors is key to delivering dependable analytical results for decision-making.