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

Outliers são aqueles pontos de dados extremamente diferentes do resto do conjunto de dados.
O teste Q de Dixon é um teste de significância que ajuda a determinar se esses pontos de dados inconsistentes devem ser retidos ou rejeitados no conjunto de dados.
Matematicamente, a estatística do teste Q é a razão entre a diferença absoluta entre o outlier e seu ponto de dados mais próximo e o intervalo da população.
A estatística do teste Q ou quociente de rejeição é então comparada com o valor Q crítico tabulado para um determinado nível de significância e graus de liberdade.
Quando o quociente de rejeição é igual ou maior que o valor tabulado, a hipótese nula é rejeitada e o outlier suspeito pode ser rejeitado.
Pelo contrário, a hipótese nula é aceita quando o valor experimental é menor que o valor Q tabulado. Em seguida, o outlier suspeito precisa ser retido no conjunto de dados.

detecção de erro bruto: o teste q

Quando um ou mais pontos de dados aparecem longe do resto dos dados, é necessário determinar se é valores atípicos e se devem ser eliminados do conjunto de dados para garantir uma representação precisa do valor medido. Em muitos casos, os valores atípicos surgem de erros grosseiros (ou erros humanos) e não refletem com precisão o fenômeno subjacente. Em alguns casos, no entanto, esses valores atípicos aparentes refletem verdadeiras diferenças fenomenológicas. Nestes casos, podemos usar métodos estatísticos para ajudar a determinar se devemos manter o valor atípico. Um método estatístico que pode nos ajudar a reter ou rejeitar valores atípico é chamado de teste Q. Para realizar o teste Q, primeiro organizamos os valores em um conjunto de dados em ordem crescente de valor. Em seguida, calculamos o valor Q tomando a razão da diferença absoluta entre um ponto de dados e seu ponto de dados adjacente. Este valor Q é então comparado com o valor Q crítico tabelado em um nível de significância escolhido e graus de liberdade apropriados. Se o valor Q for igual ou superior ao valor Q de referência na tabela, o ponto de dados é considerado um valor atípico e, portanto, rejeitado do conjunto de dados. Nesse caso, é razoável desconsiderar o ponto de dados como um valor atípico porque a magnitude do desvio não pode ser logicamente atribuída a erros aleatórios (indeterminados). Por outro lado, se o valor Q for menor que o valor de referência na tabela, o ponto de dados deve ser retido, e a interpretação é que a diferença entre esse ponto de dados e o resto dos dados está dentro da expectativa (estatística) razoável.

Detecção de Erro Bruto: O Teste Q

Outliers são aqueles pontos de dados extremamente diferentes do resto do conjunto de dados.
O teste Q de Dixon é um teste de significância que ajuda a determinar se ...

Education

JoVE Core

Analytical Chemistry

Química Analítica

Unit 1

Unidade 1

Chemical Applications of Statistical Analyses

Aplicações Químicas de Análises Estatísticas

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KEY TERMS AND CONCEPTS

TERMOS E CONCEITOS-CHAVE

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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