# shapiro.test

##### 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

##### 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.

##### See Also

`qqnorm`

for producing a normal quantile-quantile plot.

##### Examples

`library(stats)`

```
shapiro.test(rnorm(100, mean = 5, sd = 3))
shapiro.test(runif(100, min = 2, max = 4))
```

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*