Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

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

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Stump Appendicitis: A 25-Year Review of Pathophysiology, Diagnosis, and Management (2000-2025).

Cureus·2026
Same author

Incidentally Detected Low-Grade Appendiceal Mucinous Neoplasm Following Laparoscopic Appendectomy for Acute Appendicitis.

Cureus·2026
Same author

Incidentally Detected Gallbladder Adenocarcinoma Presenting as Acute Calculous Cholecystitis: A Case Report and Management Considerations.

Cureus·2026
Same author

Emergence of Local Ordering and Mesoscale Giant Number Fluctuations in Active Turbulence.

Physical review letters·2026
Same author

Feasibility and efficacy of virtual reality rehabilitation for upper extremity impairment in ischaemic stroke patients: an open-label, parallel-group, randomised controlled trial.

BMJ open·2026
Same author

Squamous Cell Carcinoma of the Gallbladder With Recurrence at the Tumor Spillage Site Following Laparoscopic Cholecystectomy.

Cureus·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Video Experimental Relacionado

Updated: Oct 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K

Algoritmos justos para la selección de las asambleas de ciudadanos

Bailey Flanigan1, Paul Gölz2, Anupam Gupta3

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

Nature
|August 5, 2021
PubMed
Resumen
Este resumen es generado por máquina.

Los nuevos algoritmos para seleccionar las asambleas de ciudadanos aseguran paneles representativos al tiempo que maximizan la probabilidad de selección igual para los participantes. Esto hace avanzar los principios de división justa en la participación cívica y las prácticas de clasificación a nivel mundial.

Más Videos Relacionados

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

14.4K
The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.3K

Videos de Experimentos Relacionados

Last Updated: Oct 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

14.4K
The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.3K

Área de la Ciencia:

  • Compromiso cívico y ciencias políticas
  • Ciencias sociales computacionales
  • Teoría de la división justa

Sus antecedentes:

  • Las asambleas de ciudadanos se utilizan cada vez más para la formulación de políticas, con la participación de ciudadanos seleccionados al azar.
  • Los procesos de selección tienen como objetivo la representatividad de la población y la misma probabilidad de selección individual.
  • Las diferencias en las tasas de participación crean una tensión entre la representatividad y la igualdad de probabilidades.

Objetivo del estudio:

  • Desarrollar nuevos algoritmos de selección para las asambleas de ciudadanos.
  • Para abordar la tensión entre la representatividad del panel y las probabilidades de selección iguales.
  • Proporcionar un método más justo y de principios para la clasificación.

Principales métodos:

  • Aplicó principios de la teoría de la división justa para crear nuevos algoritmos de selección.
  • Desarrolló algoritmos que optimizan simultáneamente la representatividad y la igualdad de probabilidad.
  • Implementó y probó un algoritmo en más de 40 asambleas de ciudadanos en todo el mundo.

Principales resultados:

  • Los algoritmos propuestos logran una mayor equidad en las probabilidades de selección en comparación con los métodos anteriores.
  • Demostró mejoras sustanciales en la equidad utilizando datos de diez asambleas de ciudadanos.
  • El algoritmo implementado se ha desplegado con éxito a nivel mundial.

Conclusiones:

  • Los principios de división equitativa ofrecen un marco sólido para mejorar la clasificación en las asambleas de ciudadanos.
  • Los algoritmos desarrollados proporcionan un enfoque más equitativo y basado en principios para la selección de participantes.
  • Este trabajo refuerza la base de la clasificación y destaca su aplicación en la división equitativa.