Some simpler statistical tests for rejecting outliers in quantitative data*
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Some simpler statistical tests for rejecting outliers in quantitative data*
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Some simpler statistical tests for rejecting outliers in quantitative data* Large data sets (N>100):
∑ For small data sets: ∑ 1 Rule of Huge Error: If you have a single outlier, then you can discard it with 98% confidence if any of the following conditions are met. |
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Dixon’s Q-test: If you have a single outlier, and your data has a normal distribution, then you can discard the outlier if . Order the data values in increasing or decreasing order, such that the outlier is the final data point (xN). 3
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*Compiled from: -Personal webpage of Prof. James K. Hardy, Dept. of Chemistry, University of Akron, “Statistical Treatment of Data” at http://ull.chemistry.uakron.edu/analytical/Statistics/. This has good notes for basic statistics and refers to specific tests for the rejection of data and discusses large and small sample sets. -“Dixon's Q-test: Detection of a single outlier”, which includes an Applet for doing Q-test calculations and a brief discussion on rejecting data from small data sets, on the University of Athen’s Department of Chemistry website at http://www.chem.uoa.gr/Applets/AppletQtest/Appl_Qtest2.html. Note: although much of the department’s website is in Greek, this page is in English. -“Statistical Treatment of Analytical Data: Outliers (Chapter 6)” by Z.B. Alfassi, Z. Boger and Y. Ronen. CRC Press: 2005. This chapter is available for reading through Google books if your library doesn’t have a copy.
