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What is Dixon’s Q Test? Dixon’s Q test, or just the “Q Test” is a way to find outliers in very small, normally distributed, data sets. Small data sets are usually defined as somewhere between 3 and 7 items.
In statistics, Dixon's Q test, or simply the Q test, is used for identification and rejection of outliers. This assumes normal distribution and per Robert Dean and Wilfrid Dixon, and others, this test should be used sparingly and never more than once in a data set.
There are several versions of Dixon’s Q-Test, each of which calculates a value for Q ij where i is the number of suspected outliers on one end of the data set and j is the number of suspected outliers on the opposite end of the data set.
May 2, 2019 · Dixon’s Q Test, often referred to simply as the Q Test, is a statistical test that is used for detecting outliers in a dataset. The test statistic for the Q test is as follows: Q = |xa – xb| / R. where xa is the suspected outlier, xb is the data point closest to xa, and R is the range of the dataset.
One of the most common approaches is called Dixon's Q-test. The basis of the Q-test is to compare the difference between the suspected outlier's value and the value of the result nearest to it (the gap) to the difference between the suspected outlier's value and the value of the result furthest from it the range).
The Dixon's Q-test is the simpler test of this type and it is usually the only one described in textbooks of Analytical Chemistry in the chapters of data treatment. This test allows us to examine if one (and only one) observation from a small set of replicate observations (typically 3 to 10) can be "legitimately" rejected or not.
Jul 19, 2014 · Dixon’s Q test [1] was “invented” as a convenient procedure to quickly identify outliers in datasets that only contains a small number of observations: typically 3 > n ≤ 10. [1] R. B. Dean and W. J. Dixon (1951) Simplified Statistics for Small Numbers of Observations”. Anal. Chem., 1951, 23 (4), 636–638. Application.
