Name: using the q test to flag outlier data
Uploaded: 2024-04-04T10:51:12.000Z
Duration: 1 min
Channel: JoVE

K12 Standards

Texas Essential Knowledge and Skills

High School

Chemistry

CHEM.2

CHEM.2.C

Detection of Gross Error: The Q Test

Los valores atípicos son aquellos puntos de datos extremadamente diferentes del resto del conjunto de datos.
La prueba Q de Dixon es una prueba de significación que ayuda a determinar si se deben retener o rechazar estos puntos de datos inconsistentes en el conjunto de datos.
Matemáticamente, el estadístico de la prueba Q es la relación entre la diferencia absoluta entre el valor atípico y su punto de datos más cercano y el rango de la población.
A continuación, se compara el estadístico de la prueba Q o el cociente de rechazo con el valor Q crítico tabulado para un nivel de significación y grados de libertad determinados.
Cuando el cociente de rechazo es igual o mayor que el valor tabulado, se rechaza la hipótesis nula y se puede rechazar el valor atípico sospechoso.
Por el contrario, se acepta la hipótesis nula cuando el valor experimental es menor que el valor Q tabulado. A continuación, el valor atípico sospechoso debe conservarse en el conjunto de datos.

detección de error grave: la prueba q

Cuando uno o más puntos de datos aparecen lejos del resto de los datos, es necesario determinar si son valores atípicos y si deben eliminarse del conjunto de datos para garantizar una representación precisa del valor medido. En muchos casos, los valores atípicos surgen de errores graves (o errores humanos) y no reflejan con precisión el fenómeno subyacente. En algunos casos, sin embargo, estos aparentes valores atípicos reflejan verdaderas diferencias fenomenológicas. En estos casos, podemos utilizar métodos estadísticos para ayudar a determinar si se debe retener el valor atípico. Un método estadístico que puede ayudarnos a retener o rechazar valores atípicos se llama prueba Q. Para realizar la prueba Q, primero ordenamos los valores de un conjunto de datos en orden creciente. Luego, calculamos el valor Q tomando la relación de la diferencia absoluta entre un punto de datos y su punto de datos adyacente. Luego, este valor Q se compara con el valor Q crítico tabulado en un nivel de significancia elegido y grados de libertad apropiados. Si el valor Q iguala o excede el valor Q de referencia en la tabla, el punto de datos se considera un valor atípico y, por lo tanto, se rechaza del conjunto de datos. En este caso, es razonable descartar el dato como un valor atípico porque la magnitud de la desviación no puede explicarse lógicamente mediante errores aleatorios (indeterminados). Por otro lado, si el valor Q es menor que el valor de referencia en la tabla, el punto de datos debe conservarse y la interpretación es que la diferencia entre este punto de datos y el resto de los datos está dentro de las expectativas (estadísticas) razonables.

Detección de error grave: La prueba Q

Los valores atípicos son aquellos puntos de datos extremadamente diferentes del resto del conjunto de datos.
La prueba Q de Dixon es una prueba de significación que ayuda a determinar si ...

Education

Educación

JoVE Core

Analytical Chemistry

Química Analítica

Unit 1

Unidad 1

Chemical Applications of Statistical Analyses

Aplicaciones Químicas de los Análisis Estadísticos

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

TÉRMINOS Y CONCEPTOS CLAVE

using the q test to flag outlier data

The Q test helps identify outlier data points that sit far from the rest of a data set. In high school lab work, this can help students decide whether an unusual value should stay in the data or be removed. Outliers often come from gross errors, or human mistakes, and may not reflect the real result being measured.

Some points that look like outliers may still reflect a true difference in the phenomenon being studied. In those cases, a statistical test can help judge whether the value should be kept or rejected. The Q test provides that check by comparing a suspect point with the rest of the ordered data.

To perform the Q test, the values are first arranged in increasing order. Next, the Q value is calculated as the ratio of the absolute difference between a data point and its adjacent data point. That Q value is then compared with the tabulated critical Q value for a chosen significance level and the correct degrees of freedom.

If the calculated Q value is equal to or greater than the table value, the point is treated as an outlier and rejected from the data set. In that case, the deviation is considered too large to be explained by random, indeterminate errors. If the Q value is smaller than the reference value, the point is retained because the difference is within normal statistical expectation.

Using the Q Test to Flag Outlier Data

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detection of gross error: the q test

The Q test helps identify outlier data points that sit far from the rest of a data set. In high school lab work, this can help students decide whether an unusual value should stay in the data or be removed. Outliers often come from gross errors, or human mistakes, and may not reflect the real result being measured. Some points that look like outliers may still reflect a true difference in the phenomenon being studied. In those cases, a statistical test can help judge whether the value should...

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key terms and concepts

Learn how the Q test helps students identify outlier data, compare a suspected value to a critical table value, and decide whether to keep or reject it.

Using the Q Test to Flag Outlier Data | JoVE K12

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Using the Q Test to Flag Outlier Data

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