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

Les valeurs aberrantes sont des points de données extrêmement différents du reste de l’ensemble de données.
Le Q-test de Dixon est un test de signification qui permet de déterminer s'il faut conserver ou rejeter ces points de données incohérents dans l'ensemble de données.
Mathématiquement, la statistique du test Q est le rapport de la différence absolue entre la valeur aberrante et son point de données le plus proche et la plage de la population.
La statistique du test Q ou le quotient de rejet est ensuite comparé à la valeur critique Q tabulée pour un niveau de signification et des degrés de liberté particuliers.
Lorsque le quotient de rejet est égal ou supérieur à la valeur tabulée, l’hypothèse nulle est rejetée et la valeur aberrante suspectée peut être rejetée.
Au contraire, l’hypothèse nulle est acceptée lorsque la valeur expérimentale est inférieure à la valeur Q tabulé. Ensuite, la valeur aberrante suspectée doit être conservée dans l’ensemble de données.

détection d'une erreur grossière : le test q

Lorsqu'un ou plusieurs points de données apparaissent éloignés du reste des données, il est nécessaire de déterminer s'il s'agit de valeurs aberrantes et s'ils doivent être éliminés de l'ensemble de données pour garantir une représentation précise de la valeur mesurée. Dans de nombreux cas, les valeurs aberrantes résultent d’erreurs grossières (ou humaines) et ne reflètent pas fidèlement le phénomène sous-jacent. Dans certains cas, cependant, ces valeurs aberrantes apparentes reflètent de véritables différences phénoménologiques. Dans ces cas, nous pouvons utiliser des méthodes statistiques pour déterminer s’il convient de conserver la valeur aberrante. Une méthode statistique qui peut nous aider à retenir ou à rejeter les valeurs aberrantes est appelée le test Q. Pour effectuer le test Q, nous classons d’abord les valeurs dans un ensemble de données par ordre croissant de valeur. Ensuite, nous calculons la valeur Q en prenant le rapport de la différence absolue entre un point de données et son point de données adjacent. Cette valeur Q est ensuite comparée à la valeur Q critique tabulée à un niveau de signification choisi et à des degrés de liberté appropriés. Si la valeur Q est égale ou supérieure à la valeur Q de référence dans le tableau, le point de données est considéré comme une valeur aberrante et est donc rejeté de l'ensemble de données. Ici, il est raisonnable de ne pas considérer le point de données comme une valeur aberrante, car l'ampleur de l'écart ne peut pas être logiquement expliquée par des erreurs aléatoires (indéterminées). D'un autre côté, si la valeur Q est inférieure à la valeur de référence dans le tableau, le point de données doit être conservé et l'interprétation est que la différence entre ce point de données et le reste des données se situe dans les limites des attentes (statistiques) raisonnables.

Détection d'une erreur grossière : le test Q

Les valeurs aberrantes sont des points de données extrêmement différents du reste de l’ensemble de données.
Le Q-test de Dixon est un test de signification qui permet de déterminer si ...

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

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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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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