Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Sampling Plans01:23

Sampling Plans

819
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...
819
Convenience Sampling Method00:55

Convenience Sampling Method

10.8K
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...
10.8K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

1.8K
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...
1.8K
Sampling Methods: Overview01:06

Sampling Methods: Overview

1.9K
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...
1.9K
Random Sampling Method01:09

Random Sampling Method

14.0K
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...
14.0K
Sampling Theorem01:15

Sampling Theorem

1.2K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Agreements and disagreements with resource-rational contractualism.

The Behavioral and brain sciences·2026
Same author

The Political Psychology of Economic Inequality.

Psychological science in the public interest : a journal of the American Psychological Society·2026
Same author

Random generation is what comes to mind in naturalistic settings.

Cognition·2026
Same author

Generated outcomes in risky choice reveal biased sampling and sequential dependencies.

Communications psychology·2026
Same author

Median effective dose (ED<sub>50</sub>) of esketamine combined with different doses of propofol for children to inhibit the response to gastroscope insertion: a prospective dose-response clinical study.

BMC anesthesiology·2026
Same author

Imagining and building wise machines: the centrality of AI metacognition.

Trends in cognitive sciences·2026
Same journal

Are language models models?

The Behavioral and brain sciences·2026
Same journal

Large language models illuminate the mechanistic underpinnings of the creative aspect of language use (CALU), long regarded as a mystery.

The Behavioral and brain sciences·2026
Same journal

LLMs as a platform for studying constraint interaction: Motivation and challenges.

The Behavioral and brain sciences·2026
Same journal

Beyond the data gap: Children create languages, violate their input statistics, and exhibit critical periods.

The Behavioral and brain sciences·2026
Same journal

Not-so-strange love: Language models and generative linguistic theories are more compatible than they appear.

The Behavioral and brain sciences·2026
Same journal

Rich data drive generalization: Lessons from machine learning for linguistics and cognitive science.

The Behavioral and brain sciences·2026
See all related articles

Related Experiment Video

Updated: Dec 26, 2025

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

35.7K

Sampling as a resource-rational constraint.

Adam N Sanborn1, Jianqiao Zhu1, Jake Spicer1

  • 1Department of Psychology, University of Warwick, CoventryCV4 7AL, United Kingdom. a.n.sanborn@warwick.ac.ukJ.Zhu@warwick.ac.ukj.spicer@warwick.ac.ukhttps://warwick.ac.uk/fac/sci/psych/people/asanborn/https://warwick.ac.uk/fac/sci/psych/people/zjianqiao/.

The Behavioral and Brain Sciences
|March 12, 2020
PubMed
Summary
This summary is machine-generated.

Resource rationality helps select models with similar cognitive constraints but not fundamental disagreements. We propose sampling as a key constraint, optimizing evidence accumulation to minimize time costs and explain behavior.

More Related Videos

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models
05:07

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

Published on: March 6, 2018

16.0K
An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.6K

Related Experiment Videos

Last Updated: Dec 26, 2025

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

35.7K
Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models
05:07

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

Published on: March 6, 2018

16.0K
An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.6K

Area of Science:

  • Cognitive Science
  • Decision Making
  • Computational Neuroscience

Background:

  • Resource rationality is a framework for evaluating cognitive strategies under constraints.
  • Existing models struggle to resolve disagreements about the nature of cognitive constraints.
  • The cost of time is a critical factor in decision-making and evidence accumulation.

Purpose of the Study:

  • To propose sampling as a fundamental cognitive constraint.
  • To demonstrate how optimizing sampling minimizes time costs in evidence accumulation.
  • To provide a framework for resolving disagreements in cognitive modeling.

Main Methods:

  • Theoretical analysis of resource-rational strategies.
  • Modeling evidence accumulation under time constraints.
  • Review of existing computational models of human behavior.

Main Results:

  • Sampling offers a compelling constraint for resource-rational analysis.
  • Optimizing evidence or hypothesis sampling minimizes the cost of time.
  • Well-established models based on sampling successfully explain human behavior.

Conclusions:

  • Sampling provides a unified approach to understanding cognitive constraints.
  • Resource rationality combined with sampling offers a powerful tool for cognitive modeling.
  • This approach has significant implications for explaining diverse human behaviors.