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

Выбросы — это точки данных, которые сильно отличаются от остального набора данных.
Q-критерий Диксона — это тест значимости, который помогает определить, следует ли сохранять или отклонять эти противоречивые точки данных в наборе данных.
Математически статистика Q-критерия — это отношение абсолютной разницы между выбросом до ближайшей точки данных и диапазоном популяции.
Затем статистика Q-теста или коэффициент отторжения сравниваются с табличным критическим значением Q для определенного уровня значимости и степеней свободы.
Когда коэффициент отбрасывания равен или больше табличного значения, нулевая гипотеза отклоняется, а предполагаемый выброс может быть отвергнут.
Напротив, нулевая гипотеза принимается, когда экспериментальное значение меньше табличного значения Q. Затем предполагаемый выброс должен быть сохранен в наборе данных.

Обнаружение грубых ошибок: q тест

Когда одна или несколько точек данных оказываются далеко от остальных данных, необходимо определить, являются ли они выбросами и следует ли их исключить из набора данных, чтобы обеспечить точное представление измеренного значения. Во многих случаях выбросы возникают в результате грубых ошибок (или человеческих ошибок) и неточно отражают основное явление. Однако в некоторых случаях эти очевидные отклонения отражают истинные феноменологические различия. В этих случаях мы можем использовать статистические методы, чтобы определить, следует ли сохранять выброс. Статистический метод, который может помочь нам сохранить или отклонить выбросы, называется Q-тестом. Чтобы выполнить тест Q, мы сначала упорядочиваем значения в наборе данных в порядке возрастания значений. Затем мы вычисляем значение Q, взяв отношение абсолютной разницы между точкой данных и соседней точкой данных. Это значение Q затем сравнивается с табличным критическим значением Q на выбранном уровне значимости и соответствующих степенях свободы. Если значение Q равно или превышает эталонное значение Q в таблице, точка данных считается выбросом и, следовательно, исключается из набора данных. Здесь разумно игнорировать точку данных как выброс, поскольку величину отклонения нельзя логически объяснить случайными (неопределенными) ошибками. С другой стороны, если значение Q меньше эталонного значения в таблице, точку данных следует сохранить, а интерпретация заключается в том, что разница между этой точкой данных и остальными данными находится в пределах разумного (статистического) ожидания.

Обнаружение грубых ошибок: Q тест

Выбросы — это точки данных, которые сильно отличаются от остального набора данных.
Q-критерий Диксона — это тест значимости, который помогает определить, ...

Education

JoVE Core

Analytical Chemistry

Аналитическая химия

Unit 1

Раздел 1

Chemical Applications of Statistical Analyses

Применение статистического анализа в химии

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

ОСНОВНЫЕ ТЕРМИНЫ И ПОНЯТИЯ

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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