You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.
| You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.
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| See also: normal distribution, chi-square test, Kolmogorov-Smirnov test |   |
The test for normality is a commonly needed procedure, since many of the statistical procedures are assumed to be applied to normally distributed data. In general, the test for normality can be achieved by applying a goodness-of-fit method (i.e. chi-square test, or Kolmogorov-Smirnov test). These two tests, however, do not perform well (the power of these tests is not too high). Therefore some other tests have been developed, which have various advantages but also some drawbacks: the power of the Shapiro-Wilk test is good, but the calculation procedure is rather cumbersome. A comparison of various tests for normality is given in [1].
| [1] | R. B. D'Agostino and M. A. Stephens (eds.)
Goodness-of-Fit Techniques 1986, pp. 367-419 |
Last Update: 2004-Jul-03