Definición de distribución de diferencias entre dos medidas muestrales.

[i][b]Definición.[br][/b][br]Es la distribución estadística que describe cómo varía la diferencia entre las medias de dos muestras independientes tomadas de dos poblaciones distintas. Esta distribución permite inferir sobre la diferencia entre las medias poblacionales basándose en las diferencias observadas en las muestras.[br][br][img]data:image/png;base64,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[/img][br]Z = Área de la distribución normal[br](p1-p2) = diferencia deseada[br]P1 = Probabilidad de éxito de la muestra 1[br]Q1 = Probabilidad de fracaso de la muestra 1[br]P2 = Probabilidad de éxito de la muestra 2[br]Q2 = Probabilidad de fracaso de la muestra 2[br]n1 = Tamaño de la muestra 1[br]n2 = Tamaño de la muestra 2[br][br][/i][b]Media de la Diferencia de Medias Muestrales[/b][br][br]La media de la diferencia entre dos medias muestrales es igual a la diferencia entre las medias poblacionales.[br][img]data:image/png;base64,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[/img] [img]data:image/png;base64,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[/img][br][br][b]Varianza y Desviación Estándar de la Diferencia de Medias Muestrales[/b][br][br]La varianza de la diferencia se calcula sumando las varianzas de cada muestra divididas por sus tamaños muestrales.[br][img]data:image/png;base64,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[/img][br][b]Distribución de la Diferencia de Medias Muestrales[/b][br][br]Si las muestras son lo suficientemente grandes o las poblaciones siguen una distribución normal, la diferencia entre las medias muestrales se distribuye aproximadamente como una distribución normal con media y desviación estándar.[br][img]data:image/png;base64,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[/img][br]

Information: Definición de distribución de diferencias entre dos medidas muestrales.