# shapiro.test

0th

Percentile

##### Shapiro-Wilk Normality Test

Performs the Shapiro-Wilk test of normality.

Keywords
htest
##### Usage
shapiro.test(x)
##### Arguments
x
a numeric vector of data values. Missing values are allowed, but the number of non-missing values must be between 3 and 5000.
##### Value

A list with class "htest" containing the following components:
statistic
the value of the Shapiro-Wilk statistic.
p.value
an approximate p-value for the test. This is said in Royston (1995) to be adequate for p.value < 0.1.
method
the character string "Shapiro-Wilk normality test".
data.name
a character string giving the name(s) of the data.

##### Source

The algorithm used is a C translation of the Fortran code described in Royston (1995) and found at http://lib.stat.cmu.edu/apstat/R94. The calculation of the p value is exact for $n = 3$, otherwise approximations are used, separately for $4 \le n \le 11$ and $n \ge 12$.

##### References

Patrick Royston (1982) An extension of Shapiro and Wilk's $W$ test for normality to large samples. Applied Statistics, 31, 115--124.

Patrick Royston (1982) Algorithm AS 181: The $W$ test for Normality. Applied Statistics, 31, 176--180.

Patrick Royston (1995) Remark AS R94: A remark on Algorithm AS 181: The $W$ test for normality. Applied Statistics, 44, 547--551.

qqnorm for producing a normal quantile-quantile plot.
library(stats) shapiro.test(rnorm(100, mean = 5, sd = 3)) shapiro.test(runif(100, min = 2, max = 4))