Which is more appropriate whether Shapiro-Wilk test, Modified SW , Jarque-Bera (JB), Modified Jarque-Bera (MJB), Ryan-Joiner (RJ), the D’Agostino Pearson Omnibus (DPO) , Anderson-Darling goodness-of-fit test, KS (Kolmogorov-Smirnov), Lilliefors test, CM (Cramer-Von Mises), WCM (weighted CM), D (modified KS), CS (chi-squared) and u (Studentized range)?
please point out your information via sharing the related research papers. Thanks in advance.
please point out your information via sharing the related research papers. Thanks in advance.
Shahul Hameed According to Field, A. P. (2009). Discovering statistics using SPSS (ed. Andy Field). London: SAGE.
- The K–S test can be used to see if a distribution of scores significantly differs from a normal distribution.
- The Shapiro–Wilk test does much the same thing, but it has more power to detect differences from normality (so, you might find this test is significant when the K–S test is not).
- Warning: In large samples these tests can be significant even when the scores are only slightly different from a normal distribution. Therefore, they should always be interpreted in conjunction with histograms, P–P or Q–Q plots, and the values of skewness and kurtosis.
Do you mean 'assessing'? Nevertheless, a reasonably large sample size is required to detect departures from normality. Only extreme types of non-normality can be detected with samples less than fifty observations. You can use a QQ plot or Kolmogorov Smirnov test to assess for normality
Ayesha Batool Thank you so much. what about large samples? any idea pls share.
Thank you Andrew Paul McKenzie Pegman , I have been reviewed Many research articles but most of the researchers not suggest Kolmogorov Smirnov test because of its worst performance.
but your view is different from others. please share the materials if you have to support KS test.
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