Shapiro–Wilk test

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The Shapiro–Wilk test is a test of normality in frequentist statistics. It was published in 1965 by Samuel Sanford Shapiro and Martin Wilk.


The Shapiro–Wilk test utilizes the null hypothesis principle to check whether a sample x1, ..., xn came from a normally distributed population. The test statistic is:

W = {\left(\sum_{i=1}^n a_i x_{(i)}\right)^2 \over \sum_{i=1}^n (x_i-\overline{x})^2}


  • x_{(i)} (with parentheses enclosing the subscript index i) is the ith order statistic, i.e., the ith-smallest number in the sample;
  • \overline{x} = \left( x_1 + \cdots + x_n \right) / n is the sample mean;
  • the constants a_i are given by[1]
(a_1,\dots,a_n) = {m^\top V^{-1} \over (m^\top V^{-1}V^{-1}m)^{1/2}}
m = (m_1,\dots,m_n)^\top\,
and m_1,\ldots,m_n are the expected values of the order statistics of independent and identically distributed random variables sampled from the standard normal distribution, and V is the covariance matrix of those order statistics. The user may reject the null hypothesis if W is below a predetermined threshold.


The null-hypothesis of this test is that the population is normally distributed. Thus 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 from a normally distributed population. In other words, the data is not normal. On the contrary, if the p-value is greater than the chosen alpha level, then the null hypothesis that the data came from a normally distributed population cannot be rejected. E.g. for an alpha level of 0.05, a data set with a p-value of 0.02 rejects the null hypothesis that the data are from a normally distributed population.[2] However, since the test is biased by sample size,[3] the test may be statistically significant from a normal distribution in any large samples. Thus a Q–Q plot is required for verification in addition to the test.

Power analysis[edit]

A research paper[4] concluded 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 2000.[5] This technique is used in several software packages including R,[6] Stata,[7] SPSS and SAS.[8]

See also[edit]


  1. ^ 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 205384.  p. 593
  2. ^ "How do I interpret the Shapiro–Wilk test for normality?". JMP. 2004. Retrieved March 24, 2012. 
  3. ^ Field, Andy (2009). Discovering statistics using SPSS : (and sex and drugs and rock 'n' roll) (3rd ed. ed.). Los Angeles [i.e. Thousand Oaks, Calif.]: SAGE Publications. p. 143. ISBN 978-1-84787-906-6. 
  4. ^ Razali, Nornadiah; Wah, Yap Bee (2011). "Power comparisons of Shapiro–Wilk, Kolmogorov–Smirnov, Lilliefors and Anderson–Darling tests". Journal of Statistical Modeling and Analytics 2 (1): 21–33. Retrieved 5 June 2012. 
  5. ^ Royston, Patrick (September 1992). "Approximating the Shapiro–Wilk W-test for non-normality". Statistics and Computing 2 (3): 117–119. doi:10.1007/BF01891203. 
  6. ^ Korkmaz, Selcuk. "Package 'royston'". Retrieved 26 February 2014. 
  7. ^ Royston, Patrick. "Shapiro–Wilk and Shapiro–Francia Tests". Stata Technical Bulletin, StataCorp LP 1 (3). 
  8. ^ Park, Hun Myoung (2002–2008). "Univariate Analysis and Normality Test Using SAS, Stata, and SPSS". [working paper]. Retrieved 26 February 2014. 

External links[edit]