shapiro.test {stats} | R Documentation |

## Shapiro-Wilk Normality Test

### Description

Performs the Shapiro-Wilk test of normality.

### 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 |

`method` |
the character string |

`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).
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.
doi:10.2307/2347973.

Patrick Royston (1982).
Algorithm AS 181: The `W`

test for Normality.
*Applied Statistics*, **31**, 176–180.
doi:10.2307/2347986.

Patrick Royston (1995).
Remark AS R94: A remark on Algorithm AS 181: The `W`

test for
normality.
*Applied Statistics*, **44**, 547–551.
doi:10.2307/2986146.

### See Also

`qqnorm`

for producing a normal quantile-quantile plot.

### Examples

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

*stats*version 4.4.0 Index]