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

이상치(outliers)는 데이터 세트의 나머지 부분과 매우 다른 데이터 포인트입니다.
Dixon의 Q-검정은 데이터 세트에서 이러한 일관되지 않은 데이터 포인트를 유지할지 또는 거부할지 여부를 결정하는 데 도움이 되는 유의성 테스트입니다.
수학적으로 Q-검정 통계량은 가장 가까운 데이터 포인트에 대한 이상치와 모집단의 범위 간의 절대 차이의 비율입니다.
그런 다음 Q-검정 통계량 또는 기각 지수를 특정 유의 수준 및 자유도에 대한 표로 만든 임계 Q 값과 비교합니다.
기각 지수가 표 형식의 값보다 크거나 같으면 귀무 가설이 기각되고 의심되는 특이치가 기각될 수 있습니다.
반대로, 귀무 가설은 실험 값이 표로 정리된 Q 값보다 작을 때 허용됩니다. 그런 다음 의심되는 이상값을 데이터 세트에 유지해야 합니다.

총 오류 탐지: q 검사

하나 이상의 자료 포인트가 나머지 자료와 멀리 떨어져 있는 경우 측정된 값을 정확하게 표현하기 위해 해당 포인트가 이상치인지, 자료 세트에서 제거해야 하는지 여부를 결정해야 합니다. 많은 경우 이상값은 심각한 오류(또는 인적 오류)로 인해 발생하며 근본적인 현상을 정확하게 반영하지 않습니다. 그러나 어떤 경우에는 이러한 명백한 이상치가 실제 현상학적 차이를 반영합니다. 이러한 경우 통계적 방법을 사용하여 이상값을 유지할지 여부를 결정하는 데 도움을 받을 수 있습니다. 이상값을 유지하거나 거부하는 데 도움이 되는 통계적 방법을 Q 검사라고 합니다. Q 검사를 수행하려면 먼저 자료 세트의 값을 값이 증가하는 순서대로 정렬합니다. 그런 다음 자료 포인트와 인접한 자료 포인트 사이의 절대 차이의 비율을 취하여 Q 값을 계산합니다. 그런 다음 이 Q 값은 선택한 유의 수준 및 적절한 자유도에서 표로 작성된 임계 Q 값과 비교됩니다. Q 값이 테이블의 기준 Q 값과 같거나 이를 초과하는 경우 자료 포인트는 이상값으로 간주되어 자료 세트에서 거부됩니다. 여기서는 편차의 크기가 무작위(불확정) 오류로 논리적으로 설명될 수 없기 때문에 자료 포인트를 이상값으로 무시하는 것이 합리적입니다. 반면, Q 값이 표의 기준 값보다 작을 경우 자료 포인트는 유지되어야 하며, 이 자료 포인트와 나머지 자료의 차이는 합리적인(통계적) 기대 범위 내에 있다는 해석입니다.

총 오류 탐지: Q 검사

이상치(outliers)는 데이터 세트의 나머지 부분과 매우 다른 데이터 포인트입니다.
Dixon의 Q-test는 여부를 결정하는 데 도움이 되는 유의성 테스트입니다.

Education

교육

JoVE Core

Analytical Chemistry

분석 화학

Unit 1

제1단원

Chemical Applications of Statistical Analyses

통계 분석의 화학적 응용

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

주요 용어 및 개념

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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