Talk:Anderson-Darling test

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"Very large sample sizes may reject the assumption of normality with only slight imperfections. But, industrial data with sample sizes of 200 and more, have easily passed the Anderson-Darling test."

This citation comes from text I wrote for the MVPstats help files. MVPstats is a statistical analysis software program. Although this software has evolved, it originally began in 1986 as a simple program to provide computation of the Anderson-Darling test statistic. The citation may be found here: http://mvpprograms.com/help/mvpstats/distributions/NormalityTestingGuidelines

This software was used quite frequently in work we were doing in industrial situations, that is why the reference is to "industrial data." I have personally tested thousands of distributions over the years, and yes the statement is accurate. Anderson-Darling is one of the more powerful test for normality. The question one is generally trying to answer in using this or any other test for normality is whether or not the data come from a distribution that can be adequately modeled with a normal distribution. As the citation suggests, with large sample sizes, there may exist slight deviations from the normal/Gaussian distribution, although the model may be adequate. And yes, I also have seen much larger samples sizes easily pass the Anderson-Darling test.

Mvpetrovich 17:09, 26 March 2007 (UTC)

According to the Stephens (1974) article cited in the reference section, the actual sample size correction is A^2* = A^2 * (1 + 4/n - 25/n^2) and the 5% statistic for normality is 0.787.

Who is right?

141.211.198.75 (talk) 20:40, 18 February 2008 (UTC) Kayhan Gultekin kayhan aht gmail daht com