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Related Concept Videos

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.
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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.
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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 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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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Flying Insect Detection and Classification with Inexpensive Sensors
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Effect of separate sampling on classification accuracy.

Mohammad Shahrokh Esfahani1, Edward R Dougherty

  • 1Department of Electrical and Computer Engineering and Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA.

Bioinformatics (Oxford, England)
|November 22, 2013
PubMed
Summary
This summary is machine-generated.

Separate sampling in classification, where class sizes are predetermined, can significantly reduce classifier accuracy. This study introduces a minimax sampling ratio to mitigate this issue, improving classification performance.

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Area of Science:

  • Machine Learning
  • Statistical Classification

Background:

  • Traditional classification methods often use separate sampling, fixing class sizes.
  • This approach prevents accurate estimation of class prior probabilities.
  • Predetermined class sizes can negatively impact classifier performance, even with large datasets.

Purpose of the Study:

  • To investigate the detrimental effects of separate sampling on classification rules.
  • To develop a method for determining an optimal sampling ratio to improve classifier performance.

Main Methods:

  • Simulations using synthetic and real data to evaluate classification rules under separate sampling.
  • Derivation of a sample-based minimax sampling ratio.
  • Development of an algorithm to approximate the minimax sampling ratio from data.
  • Extension of the Anderson linear discriminant analysis minimax sampling ratio to arbitrary distributions.

Main Results:

  • Separate sampling significantly degrades the performance of various classification rules.
  • Propositions were established linking sampling ratios to expected classifier error.
  • A sample-based minimax sampling ratio was derived and an approximation algorithm was provided.
  • The classical minimax sampling ratio was extended for broader applicability.

Conclusions:

  • Separate sampling is a critical issue in classification that can be addressed by adjusting sampling ratios.
  • The proposed minimax sampling ratio and approximation algorithm offer a practical solution to improve classification accuracy.
  • The findings are applicable to a wide range of classification problems and distributions.