Name: grubbs test for spotting data outliers
Uploaded: 2024-04-04T10:51:12.000Z
Duration: 1 min 2 s
Channel: JoVE

K12 Standards

Texas Essential Knowledge and Skills

High School

Chemistry

CHEM.2

CHEM.2.C

quantifying-and-rejecting-outliers-the-grubbs-test

Quantifying and Rejecting Outliers: The Grubbs Test

Le test de Grubbs, comme le test Q de Dixon, est un test statistique permettant d'identifier les valeurs aberrantes dans les données ayant une distribution normale. Ici, le nombre d’observations doit être égal ou supérieur à sept.
Pour un ensemble de données donné, ce test consiste à calculer la statistique de Grubbs G, qui est le rapport de la différence absolue entre la valeur douteuse et la valeur moyenne et l'écart-type de l'échantillon.
La valeur G calculée est ensuite comparée à la valeur critique tabalée de G pour un niveau de confiance donné et le nombre d’observations.
Si la valeur G calculée dépasse la valeur critique de G, l’observation douteuse est considérée comme une valeur aberrante des données et rejetée. 
Alternativement, si la valeur G calculée est inférieure à la valeur critique de G, les données conservent l’observation douteuse.

quantifier et rejeter les valeurs aberrantes : le test de grubbs

Parfois, un ensemble de données peut contenir une observation numérique enregistrée qui s’écarte considérablement du reste des données. En supposant que les données sont distribuées normalement, une méthode statistique appelée test de Grubbs peut être utilisée pour déterminer si l'observation est réellement une valeur aberrante. Pour effectuer un test de Grubbs bilatéral, calculez d’abord la différence absolue entre la valeur aberrante et la moyenne. Calculez ensuite le rapport entre cette différence et l’écart type de l’échantillon. Ce nombre est connu sous le nom de statistique de Grubbs, « G ». Lorsque la valeur G calculée dépasse la valeur critique G pour un niveau de confiance et un nombre d'observations donnés, l'observation douteuse est considérée comme une valeur aberrante et supprimée de l'ensemble de données. Au contraire, si la valeur G calculée est inférieure à la valeur G critique, l’observation douteuse n’est pas considérée comme une valeur aberrante et est donc conservée dans l’ensemble de données.

Quantifier et rejeter les valeurs aberrantes : le test de Grubbs

Le test de Grubbs, comme le test Q de Dixon, est un test statistique permettant d'identifier les valeurs aberrantes dans les données ayant une distribution normale. Ici, le nombre de ...

Education

Enseignement

JoVE Core

Analytical Chemistry

Chimie analytique

Unit 1

Unité 1

Chemical Applications of Statistical Analyses

Applications chimiques des analyses statistiques

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

TERMES ET CONCEPTS CLÉS

grubbs test for spotting data outliers

The Grubbs test is a statistical way to spot a possible outlier in a data set. It is used when the data is assumed to follow a normal distribution. A Grubbs test helps decide whether one unusual numerical value should be treated as a true outlier.

To run a two-tailed Grubbs test, first find the absolute difference between the questionable value and the mean. Then divide that difference by the sample standard deviation. The result is the Grubbs statistic, or G.

Next, compare the calculated G value with the G critical value for the chosen confidence level and the number of observations. If the calculated G is larger, the questionable value is considered an outlier and removed from the data set. If the calculated G is smaller, the value is not considered an outlier and is kept in the data set.

Grubbs Test for Spotting Data Outliers

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The Grubbs test is a statistical way to spot a possible outlier in a data set. It is used when the data is assumed to follow a normal distribution. A Grubbs test helps decide whether one unusual numerical value should be treated as a true outlier. To run a two-tailed Grubbs test, first find the absolute difference between the questionable value and the mean. Then divide that difference by the sample standard deviation. The result is the Grubbs statistic, or G. Next, compare the calculated G...

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key terms and concepts

Learn how the Grubbs test checks a normal data set for a possible outlier by comparing the G statistic with the critical value.

Grubbs Test for Spotting Data Outliers | JoVE K12

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Grubbs Test for Spotting Data Outliers

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