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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.
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. 
In analytical chemistry, the choice of...
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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.
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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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.
To choose a stratified sample, divide the population into groups called strata and then take a...
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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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Sequential Sampling Models in Cognitive Neuroscience: Advantages, Applications, and Extensions.

B U Forstmann1, R Ratcliff2, E-J Wagenmakers3

  • 1Amsterdam Brain and Cognition Center, University of Amsterdam, 1018 WS Amsterdam, The Netherlands;

Annual Review of Psychology
|September 23, 2015
PubMed
Summary
This summary is machine-generated.

Sequential sampling models, like the diffusion decision model, explain how people make fast decisions by accumulating evidence. This model is increasingly used in cognitive neuroscience to understand the brain basis of decision-making under time pressure.

Keywords:
decision makingdiffusion decision modeldrift rateinformation accumulationresponse timespeed-accuracy trade-off

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Area of Science:

  • Cognitive Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Sequential sampling models describe decision-making as evidence accumulation.
  • The diffusion decision model (DDM) quantifies cognitive processes like information quality and response caution.
  • DDM is increasingly applied in cognitive neuroscience to study neural mechanisms of decision-making.

Purpose of the Study:

  • To provide an overview of recent applications and extensions of the diffusion decision model.
  • To highlight the utility of DDM in cognitive neuroscience research.
  • To explore how DDM aids in understanding the neural basis of decision-making under time constraints.

Main Methods:

  • Review of recent literature on diffusion decision model applications.
  • Selective overview of extensions to the diffusion decision model.
  • Focus on studies investigating the neural underpinnings of decision-making.

Main Results:

  • The diffusion decision model is a versatile tool for cognitive neuroscience.
  • Recent extensions enhance the model's ability to capture complex decision dynamics.
  • DDM applications reveal insights into the neural processes supporting speeded decisions.

Conclusions:

  • The diffusion decision model is a valuable quantitative framework in cognitive neuroscience.
  • Its application facilitates a deeper understanding of the neural basis of decision-making.
  • Continued development and application of DDM will advance the study of cognition and behavior.