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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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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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What are Estimates?01:06

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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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.
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Quartile01:15

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Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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Updated: Jan 7, 2026

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Estimación de la Mediana con Transformaciones Cuantílicas: Aplicaciones al Muestreo Estratificado en Dos Fases

Fatimah A Almulhim1, Hassan M Aljohani2

  • 1Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Entropy (Basel, Switzerland)
|December 24, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Nuevos estimadores de mediana basados en cuantil mejoran la precisión y robustez en el muestreo estratificado. Estos métodos ofrecen mayor precisión y efectividad para la estimación práctica de la mediana, especialmente con datos asimétricos.

Palabras clave:
simulación de Monte Carloinformación auxiliarsesgoerrores cuadráticos mediosestimación de la medianatransformaciones cuantílicaseficiencia relativamuestreo estratificado en dos fases

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Área de la Ciencia:

  • Estadística
  • Metodología de Encuestas

Sus antecedentes:

  • Los estimadores de mediana tradicionales a menudo asumen normalidad y son sensibles a valores atípicos.
  • Esta sensibilidad limita su fiabilidad en aplicaciones del mundo real con datos no normales o asimétricos.

Objetivo del estudio:

  • Introducir novedosos estimadores de mediana basados en cuantil.
  • Mejorar la precisión y robustez en el muestreo estratificado en dos fases.
  • Mejorar la eficiencia de la estimación de la mediana utilizando datos auxiliares.

Principales métodos:

  • Se utilizaron métodos de transformación dentro de un marco de muestreo estratificado en dos fases.
  • Se desarrollaron estimadores de mediana basados en cuantil.
  • Se derivaron expresiones de sesgo y error cuadrático medio (MSE) mediante aproximaciones de primer orden.
  • Se evaluó la eficiencia del estimador utilizando el MSE.

Principales resultados:

  • Los estimadores propuestos demostraron un rendimiento superior en simulaciones bajo distribuciones asimétricas.
  • El análisis en conjuntos de datos de poblaciones reales confirmó la efectividad de los nuevos métodos.
  • Los estimadores basados en cuantil lograron una mayor precisión y efectividad en comparación con los enfoques existentes.

Conclusiones:

  • Los novedosos estimadores de mediana basados en cuantil son robustos y precisos para aplicaciones prácticas.
  • Estos estimadores proporcionan una alternativa más efectiva para la estimación de la mediana, particularmente en escenarios de muestreo estratificado.
  • Los métodos mejoran la utilidad de los datos auxiliares y funcionan bien con estratos heterogéneos.