Dixon's Q test

In statistics, Dixon's Q test, or simply the Q test, is used for identification and rejection of outliers. ^[1]

6 relations: Absolute difference, Confidence interval, Grubbs' test for outliers, One- and two-tailed tests, Outlier, Statistics.

Absolute difference

The absolute difference of two real numbers x, y is given by |x − y|, the absolute value of their difference.

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Confidence interval

In statistics, a confidence interval (CI) is a type of interval estimate, computed from the statistics of the observed data, that might contain the true value of an unknown population parameter.

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Grubbs' test (named after Frank E. Grubbs, who published the test in 1950), also known as the maximum normalized residual test or extreme studentized deviate test, is a statistical test used to detect outliers in a univariate data set assumed to come from a normally distributed population.

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One- and two-tailed tests

In statistical significance testing, a one-tailed test and a two-tailed test are alternative ways of computing the statistical significance of a parameter inferred from a data set, in terms of a test statistic.

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Outlier

In statistics, an outlier is an observation point that is distant from other observations.

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Statistics

Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.

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Dixon Q test, Q test, Q-test.

References

[1] https://en.wikipedia.org/wiki/Dixon's_Q_test

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