Grubbs' Test (Outlier)
Computes Grubbs' test, used to identify whether the most extreme value in a sample is a statistical outlier, under the assumption that the data would be normal without it. It compares the deviation of the farthest point from the mean, in standard-deviation units, against a critical value derived from the t distribution. If the G statistic exceeds the critical value, the point is flagged as an outlier. It's the classic test for cleaning data before an analysis. Enter the data and the confidence level.
Resultado
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Grubbs' Test (Outlier)
Computes Grubbs' test, used to identify whether the most extreme value in a sample is a statistical outlier, under the assumption that the data would be normal without it. It compares the deviation of the farthest point from the mean, in standard-deviation units, against a critical value derived from the t distribution. If the G statistic exceeds the critical value, the point is flagged as an outlier. It's the classic test for cleaning data before an analysis. Enter the data and the confidence level.
Is that off-key point really an error?
Almost every data collection produces, now and then, a value that jumps out for being too far from the rest. The temptation to simply delete it is strong, but dangerous: discarding data on intuition biases the analysis. Grubbs' test offers an objective criterion for deciding whether the most extreme point deserves to be treated as an outlier.
The test measures how far from the mean the most distant value sits, counted in standard deviations, and calls that the G statistic. That number is compared with a critical value that depends on the sample size and the confidence level, derived from the t distribution. If G exceeds the critical value, the deviation is too large to be attributed to normal variation and the point is flagged as an outlier.
Enter the data and the confidence level. The tool computes G, the critical value and indicates whether the most extreme point is an outlier. Two caveats matter: the test assumes the data, without the suspect, would be normal, and it was designed to detect one outlier at a time — for more than one, there are specific variants that should be preferred.
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The results provided by this tool are for general informational and educational purposes only and do not constitute professional, financial, medical, legal, tax or accounting advice. Always confirm important decisions with a qualified professional and official sources.