I recently was faced with the impossible task to identify outliers in a dataset with very, very small sample sizes and Dixon's Q test caught my attention. Honestly, I am not a big fan of this statistical test, but since Dixon's Q-test is still quite popular in certain scientific fields (e.g., biology and chemistry) that it is important to understand its principles in order to draw your own conclusion of the presented research data that you might stumble upon in research articles or scientific talks.
I am curious: Have you used this test? How do you think about it?
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se.raschka
Well as for me, the whole idea of removing outliers in n=3..10 samples seems somewhat corrupt :) If you have a doubt that there could be an outlier - repeat an experiment or use non-parametric test..
Especially in the case of n=3 with 1 "potential" outlier: Who is the outlier here? The 1 measurement or the other 2?
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written
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se.raschka
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