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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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Sampling Distribution01:12

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Random Sampling Method01:09

Random 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. 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

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

Sampling Methods: Overview

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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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Cluster Sampling Method01:20

Cluster Sampling Method

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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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Related Experiment Video

Updated: Jan 14, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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Using Simulations to Explore Sampling Distributions: An Antidote to Hasty and Extravagant Inferences.

Guillaume A Rousselet1

  • 1School of Neuroscience & Psychology, University of Glasgow, United Kingdom Guillaume.Rousselet@glasgow.ac.uk.

Eneuro
|October 23, 2025
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Summary

Simulations clarify frequentist statistics by illustrating sampling distributions, revealing uncertainty in single experiments and highlighting how p-hacking inflates false positives in neuroscience and psychology research.

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ERPcorrelationestimationmeasurement precisionreaction timesstatistical power

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Area of Science:

  • Neuroscience
  • Psychology
  • Statistical Inference

Background:

  • Frequentist statistics, common in neuroscience and psychology, rely on sampling distributions.
  • Sampling distributions are often poorly understood and underutilized in statistical inference.

Purpose of the Study:

  • To demonstrate using simulations to visualize sampling distributions.
  • To answer practical questions about experimental outcomes and the interpretation of single-experiment results.

Main Methods:

  • Utilizing simulations (a priori and a posteriori) to illustrate sampling distributions.
  • Focusing on graphical descriptions with examples from correlation, proportion, and response latency data.

Main Results:

  • Simulations reveal the uncertainty inherent in experimental estimations.
  • They highlight that results from a single experiment should often be interpreted cautiously.
  • Demonstrates how arbitrary p-value cutoffs (p ≤ 0.05) contribute to a literature with inflated false positive rates.

Conclusions:

  • Simulations enhance understanding of the data-generating process and variability.
  • Emphasizes the need for careful interpretation of single experimental findings.
  • Warns against over-reliance on statistical tools leading to widespread false positives.