# anscombe.test

From moments v0.14
by Lukasz Komsta

##### Anscombe-Glynn test of kurtosis

Performs Anscombe-Glynn test of kurtosis for normal samples

- Keywords
- htest

##### Usage

`anscombe.test(x, alternative = c("two.sided", "less", "greater"))`

##### Arguments

- x
- a numeric vector of data values.
- alternative
- a character string specifying the alternative hypothesis, must be one of '"two.sided"' (default), '"greater"' or '"less"'. You can specify just the initial letter.

##### Details

Under the hypothesis of normality, data should have kurtosis equal to 3. This test has such null hypothesis and is useful to detect a significant difference of kurtosis in normally distributed data.

##### Value

- A list with class
- statistic
- the list containing kurtosis estimator and its transformation.
- p.value
- the p-value for the test.
- alternative
- a character string describing the alternative hypothesis.
- method
- a character string indicating what type of test was performed.
- data.name
- name of the data argument.

`htest`

containing the following components:##### References

Anscombe, F.J., Glynn, W.J. (1983) Distribution of kurtosis statistic for normal statistics. Biometrika, 70, 1, 227-234

##### See Also

##### Examples

```
set.seed(1234)
x = rnorm(1000)
kurtosis(x)
anscombe.test(x)
```

*Documentation reproduced from package moments, version 0.14, License: GPL (>= 2)*

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