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Related Concept Videos

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...
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Sampling Plans01:23

Sampling Plans

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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...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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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...
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Sampling Distribution01:12

Sampling Distribution

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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...
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Random Sampling Method01:09

Random Sampling Method

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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...
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Systematic Sampling Method01:17

Systematic Sampling Method

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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...
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Updated: Nov 17, 2025

Deploying Community Scientists to Conduct Nondestructive Genetic Sampling of Rare Butterfly Populations
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Tapping Diversity From the Wild: From Sampling to Implementation.

Sariel Hübner1, Michael B Kantar2

  • 1Galilee Research Institute (MIGAL), Tel-Hai College, Qiryat Shemona, Israel.

Frontiers in Plant Science
|February 15, 2021
PubMed
Summary

Crop wild relatives (CWRs) offer valuable traits for crop improvement but contain linked detrimental variation. Optimizing sampling and utilizing advanced technologies are crucial for effectively harnessing CWR genetic diversity in breeding programs.

Keywords:
breedingcrop wild relativegenetic dragintrogressionsampling design

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Area of Science:

  • Plant genetics and breeding
  • Agronomy
  • Conservation biology

Background:

  • Crop wild relatives (CWRs) possess valuable genetic diversity for adapting crops to harsh environments.
  • However, CWRs also contain linked detrimental genetic variation, posing challenges for breeding.
  • Current breeding efforts often overlook critical statistical and technical considerations for CWR utilization.

Purpose of the Study:

  • To review the process of sampling and identifying beneficial genetic variation in CWRs.
  • To discuss the challenges associated with using CWRs in crop breeding.
  • To highlight the role of technological advancements in exploiting CWR genetic resources.

Main Methods:

  • Review of existing literature on CWR sampling, characterization, and breeding implementation.
  • Analysis of factors influencing the detection of beneficial alleles in CWRs, including sampling design and linkage disequilibrium.
  • Discussion of the impact of technological advances in genomics, phenomics, and data science.

Main Results:

  • Sampling design significantly impacts the ability to detect beneficial genetic variation in CWRs.
  • Linkage disequilibrium poses a constraint on identifying beneficial alleles and managing genetic drag.
  • Technological advancements offer improved strategies for CWR genetic variation discovery and utilization.

Conclusions:

  • Strategic sampling and advanced analytical approaches are essential for maximizing the benefits of CWRs.
  • Overcoming challenges like genetic drag is key to successful CWR introgression.
  • Harnessing CWRs through modern technologies is vital for developing higher-yielding and sustainable crops.