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相关概念视频

Group Design02:01

Group Design

9.8K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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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...
346
Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Stratified Sampling Method01:16

Stratified 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. 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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Systematic Sampling Method01:17

Systematic Sampling Method

11.6K
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.
Systematic sampling is one of the simplest methods...
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相关实验视频

Updated: Oct 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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选择公民集会的公平算法

Bailey Flanigan1, Paul Gölz2, Anupam Gupta3

  • 1Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA. bflaniga@cs.cmu.edu.

Nature
|August 5, 2021
PubMed
概括
此摘要是机器生成的。

选择公民集会的新算法确保了代表性,同时最大限度地提高了参与者的平等选择概率. 这促进了全球公民参与和分拣的公平分配原则.

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The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
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相关实验视频

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科学领域:

  • 公民参与与政治科学
  • 计算社会科学
  • 公平分配理论

背景情况:

  • 公民大会越来越多地用于制定政策,涉及随机选择的公民.
  • 选择过程的目标是人口的代表性和个人选择的平等概率.
  • 不同的参与率在代表性和同等概率之间造成了紧张关系.

研究的目的:

  • 为公民大会开发新的选择算法.
  • 解决小组代表性和同等选择概率之间的紧张关系.
  • 提供一个更公平,更有原则的分类方法.

主要方法:

  • 应用公平划分理论的原则来创建新的选择算法.
  • 开发出可以同时优化代表性和概率平等的算法.
  • 在全球40多个公民集会上实施和测试了一种算法.

主要成果:

  • 与以前的方法相比,拟议的算法在选择概率方面实现了更高的公平性.
  • 通过使用十个公民大会的数据, 证明了公平的实质性改善.
  • 该算法已在全球成功部署.

结论:

  • 公平分配的原则为改善公民集会的分类提供了坚实的框架.
  • 开发的算法为参与者选择提供了更公平,更有原则的方法.
  • 这项工作加强了分类的基础,并强调了其在公平分配中的应用.