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Related Concept Videos

Random Sampling Method01:09

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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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Convenience Sampling Method00:55

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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.
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Cluster Sampling Method01:20

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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...
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Systematic Sampling Method01:17

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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.
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Stratified Sampling Method01:16

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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. 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.
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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.
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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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We need to talk about nonprobability samples.

Robin J Boyd1, Gary D Powney1, Oliver L Pescott1

  • 1UK Centre for Ecology and Hydrology, Benson Lane, Crowmarsh Gifford, Oxfordshire, OX10 8BB, UK.

Trends in Ecology & Evolution
|February 12, 2023
PubMed
Summary
This summary is machine-generated.

Probability sampling ensures unbiased data, but nonprobability samples are increasingly used in big data. While risky, nonprobability samples can be valuable if limitations are carefully assessed and communicated.

Keywords:
biodiversity monitoringcitizen scienceconvenience samplerisk-of-biassample representativenessselection bias

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Area of Science:

  • Ecology
  • Statistics
  • Data Science

Background:

  • Probability sampling is crucial for unbiased inference when censuses are impossible.
  • The rise of big data has led to increased use of nonprobability samples with unknown mechanisms.
  • Nonprobability samples have been controversial in biodiversity monitoring.

Purpose of the Study:

  • To explain the risks and potential utility of nonprobability samples in ecological research.
  • To review controversies surrounding nonprobability sampling in biodiversity monitoring.
  • To provide guidance on the appropriate use of nonprobability samples.

Main Methods:

  • Review of statistical principles regarding sampling bias.
  • Analysis of the implications of unknown sampling mechanisms.
  • Case study review of nonprobability sampling in biodiversity monitoring.

Main Results:

  • Nonprobability samples can lead to spurious conclusions and may have limited effective sample sizes.
  • Controversies exist regarding their application in ecological studies.
  • Despite risks, nonprobability samples can be useful if limitations are rigorously assessed and communicated.

Conclusions:

  • Nonprobability samples require careful assessment, mitigation of limitations, and transparent communication.
  • Ecologists can benefit from strategies used in other disciplines for handling nonprobability samples.
  • Responsible use of nonprobability samples can complement traditional methods in ecological research.