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関連する概念動画

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...
9.8K
Random Sampling Method01:09

Random Sampling Method

13.1K
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...
13.1K
Sampling Plans01:23

Sampling Plans

346
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

8.3K
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...
8.3K
Stratified Sampling Method01:16

Stratified Sampling Method

13.5K
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...
13.5K
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...
11.6K

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Updated: Oct 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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市民の集会を選ぶための公正なアルゴリズム

Bailey Flanigan1, Paul Gölz2, Anupam Gupta3

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

Nature
|August 5, 2021
PubMed
まとめ
この要約は機械生成です。

市民会議の選出のための新しいアルゴリズムにより 代表的なパネルが確保され,参加者の選出の可能性が最大化されます. 市民の参加と配分の原則を 世界的に推進しています

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科学分野:

  • 市民の関与と政治学
  • コンピュータ社会科学
  • 公平な分割理論

背景:

  • ランダムに選ばれた市民が参加する市民集会が政策決定にますます利用されています.
  • 選択プロセスは,集団の代表性と,個々の選択の確率の平等を目指します.
  • 参加率の差は 代表性と確率の平等の間の緊張を生み出します

研究 の 目的:

  • 市民集会のための新しい選択アルゴリズムを開発する.
  • パネルの代表性と 平等な選択確率の間の緊張を解決する.
  • より公正で 原則に基づいた分類方法を 提供するためです

主な方法:

  • 公平な分割理論の原則を適用し,新しい選択アルゴリズムを作成しました.
  • 代表性と確率の平等を同時に最適化するアルゴリズムを開発した.
  • 世界中で40以上の市民集会で アルゴリズムを導入し テストしました

主要な成果:

  • 提案されたアルゴリズムは,以前の方法と比較して,選択確率の公平性を高めます.
  • 10人の市民会議のデータを用いて 公平性の大幅な改善を証明しました
  • このアルゴリズムは 世界的に成功しています

結論:

  • 公平な分配の原則は 市民の集会での配分を改善するための 堅固な枠組みを提供します
  • 開発されたアルゴリズムは,参加者を選択するより公平で原則に基づいたアプローチを提供します.
  • この研究は,配分の基盤を強化し,公正な配分におけるその適用を強調しています.