Name: grubbs test for spotting data outliers
Uploaded: 2024-04-04T10:51:12.000Z
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Chemistry

CHEM.2

CHEM.2.C

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

Quantifying and Rejecting Outliers: The Grubbs Test

Grubbs&#39; test, like Dixon&#39;s Q-test, is a statistical test to identify the outliers in data with a normal distribution. Here, the number of observations should equal or exceed seven.
For a given data set, this test involves calculating the Grubbs&#39; statistic G, which is the ratio of the absolute difference between the questionable value and the mean value to the standard deviation of the sample.
The calculated G value is then compared with the tabulated critical value of G for a given confidence level and the number of observations.
If the calculated G value exceeds the critical value of G, the questionable observation is considered an outlier of the data and rejected.&#160;
Alternatively, if the calculated G value is smaller than the critical value of G, the data retains the questionable observation.

quantifying and rejecting outliers: the grubbs test

Sometimes, a data set can have a recorded numerical observation that greatly&#160; deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.&#160; To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is known as the Grubbs statistic, &#39;G.&#39; When the calculated G value exceeds the G critical value for a given confidence level and the number of observations, the questionable observation is considered an outlier and removed from the data set. On the contrary, if the calculated G value is smaller than the critical G value, the questionable observation is not considered an outlier and therefore retained in the data set.

Grubbs&#39; test, like Dixon&#39;s Q-test, is a statistical test to identify the outliers in data with a normal distribution. Here, the number of ...

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JoVE Core

Analytical Chemistry

Unit 1

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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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