# scipy.special.smirnovi#

scipy.special.smirnovi(n, p, out=None) = <ufunc 'smirnovi'>#

Inverse to `smirnov`

Returns d such that `smirnov(n, d) == p`, the critical value corresponding to p.

Parameters:
nint

Number of samples

pfloat array_like

Probability

outndarray, optional

Optional output array for the function results

Returns:
scalar or ndarray

The value(s) of smirnovi(n, p), the critical values.

`smirnov`

The Survival Function (SF) for the distribution

`scipy.stats.ksone`

Provides the functionality as a continuous distribution

`kolmogorov`, `kolmogi`

Functions for the two-sided distribution

`scipy.stats.kstwobign`

Two-sided Kolmogorov-Smirnov distribution, large n

Notes

`smirnov` is used by stats.kstest in the application of the Kolmogorov-Smirnov Goodness of Fit test. For historical reasons this function is exposed in scpy.special, but the recommended way to achieve the most accurate CDF/SF/PDF/PPF/ISF computations is to use the stats.ksone distribution.

Examples

```>>> from scipy.special import smirnovi, smirnov
```
```>>> n = 24
>>> deviations = [0.1, 0.2, 0.3]
```

Use `smirnov` to compute the complementary CDF of the Smirnov distribution for the given number of samples and deviations.

```>>> p = smirnov(n, deviations)
>>> p
array([0.58105083, 0.12826832, 0.01032231])
```

The inverse function `smirnovi(n, p)` returns `deviations`.

```>>> smirnovi(n, p)
array([0.1, 0.2, 0.3])
```