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Related Concept Videos

Convenience Sampling Method00:55

Convenience 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.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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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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Sampling Plans01:23

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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: Overview01:06

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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. 
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Sampling Methods: Sample Types01:18

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Sampling materials are classified into three main types: solid, liquid, and gas.
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Contaminants and Errors01:16

Contaminants and Errors

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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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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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Efficient sampling and noisy decisions.

Joseph A Heng1, Michael Woodford2, Rafael Polania1

  • 1Department of Health Sciences and Technology, Federal Institute of Technology (ETH), Zurich, Switzerland.

Elife
|September 15, 2020
PubMed
Summary
This summary is machine-generated.

Human decisions are imprecise due to limited information. This study reveals that cognitive processes efficiently adapt to environmental information, balancing accuracy with biological constraints on information coding.

Keywords:
decision makinghumaninformation theorymemoryneuroscienceresource limitationsrewardsampling

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Last Updated: Dec 9, 2025

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Decision Theory

Background:

  • Human decision-making relies on incomplete information, leading to inherent imprecision.
  • The factors influencing the degree of this imprecision in cognitive processes are not fully understood.
  • Existing models often assume precise prior knowledge, which may not reflect biological limitations.

Purpose of the Study:

  • To develop an efficient coding framework for higher-level cognitive processes.
  • To characterize the optimal information sampling process that maximizes accuracy under biological constraints.
  • To investigate how the cost of contextual information influences decision-making strategies.

Main Methods:

  • Development of an efficient coding framework representing information via discrete samples.
  • Mathematical characterization of sampling processes under different assumptions of environmental information availability and cost.
  • Empirical testing of the theoretical model using a numerosity discrimination task.

Main Results:

  • Humans demonstrate efficient adaptation to environmental distributions in decision-making.
  • Observed human behavior aligns with a model that economizes on environmental information due to biological restrictions.
  • The degree of imprecision in human decisions is influenced by the efficiency of information coding.

Conclusions:

  • Biological restrictions on information coding are crucial for understanding decision behavior.
  • The assumption of precise prior knowledge in higher-level decision systems should be re-evaluated.
  • An efficient coding framework provides insights into the trade-offs between perceptual accuracy and information processing costs.