- (with parentheses enclosing the subscript index i; not to be confused with ) is the ith order statistic, i.e., the ith-smallest number in the sample;
- is the sample mean.
The coefficients are given by:
and the vector m,
is made of the expected values of the order statistics of independent and identically distributed random variables sampled from the standard normal distribution; finally, is the covariance matrix of those normal order statistics.
The null-hypothesis of this test is that the population is normally distributed. Thus, on the one hand, if the p-value is less than the chosen alpha level, then the null hypothesis is rejected and there is evidence that the data tested are not normally distributed. On the other hand, if the p-value is greater than the chosen alpha level, then the null hypothesis that the data came from a normally distributed population can not be rejected (e.g., for an alpha level of 0.05, a data set with a p-value of less than 0.05 rejects the null hypothesis that the data are from a normally distributed population). Like most statistical significance tests, if the sample size is sufficiently large this test may detect even trivial departures from the null hypothesis (i.e., although there may be some statistically significant effect, it may be too small to be of any practical significance); thus, additional investigation of the effect size is typically advisable, e.g., a Q–Q plot in this case.
Monte Carlo simulation has found that Shapiro–Wilk has the best power for a given significance, followed closely by Anderson–Darling when comparing the Shapiro–Wilk, Kolmogorov–Smirnov, Lilliefors and Anderson–Darling tests.
Royston proposed an alternative method of calculating the coefficients vector by providing an algorithm for calculating values, which extended the sample size to 2,000. This technique is used in several software packages including Stata, SPSS and SAS. Rahman and Govidarajulu extended the sample size further up to 5,000.
- Anderson–Darling test
- Cramér–von Mises criterion
- D'Agostino's K-squared test
- Kolmogorov–Smirnov test
- Lilliefors test
- Normal probability plot
- Shapiro-Francia test
- Shapiro, S. S.; Wilk, M. B. (1965). "An analysis of variance test for normality (complete samples)". Biometrika. 52 (3–4): 591–611. doi:10.1093/biomet/52.3-4.591. JSTOR 2333709. MR 0205384. p. 593
- "How do I interpret the Shapiro–Wilk test for normality?". JMP. 2004. Retrieved March 24, 2012.
- Field, Andy (2009). Discovering statistics using SPSS (3rd ed.). Los Angeles [i.e. Thousand Oaks, Calif.]: SAGE Publications. p. 143. ISBN 978-1-84787-906-6.
- Razali, Nornadiah; Wah, Yap Bee (2011). "Power comparisons of Shapiro–Wilk, Kolmogorov–Smirnov, Lilliefors and Anderson–Darling tests" (PDF). Journal of Statistical Modeling and Analytics. 2 (1): 21–33. Retrieved 30 March 2017.
- Royston, Patrick (September 1992). "Approximating the Shapiro–Wilk W-test for non-normality". Statistics and Computing. 2 (3): 117–119. doi:10.1007/BF01891203.
- Royston, Patrick. "Shapiro–Wilk and Shapiro–Francia Tests". Stata Technical Bulletin, StataCorp LP. 1 (3).
- Shapiro–Wilk and Shapiro–Francia tests for normality
- Park, Hun Myoung (2002–2008). "Univariate Analysis and Normality Test Using SAS, Stata, and SPSS" (PDF). [working paper]. Retrieved 26 February 2014.
- Rahman und Govidarajulu (1997). "A modification of the test of Shapiro and Wilk for normality". Journal of Applied Statistics. 24 (2): 219–236. doi:10.1080/02664769723828.