_p_value#

normtest.looney_gulledge._p_value(statistic, sample_size)[source]#

This function estimates the probability associated with the Looney-Gulledge Normality test [1].

Parameters:
statisticfloat (positive)

The test statistic;

sample_sizeint

The sample size. Must be equal or greater than 4;

Returns:
p_valuefloat or str

The probability of the test;

See also

test

Notes

The test probability is estimated through linear interpolation of the test statistic with critical values from the Looney-Gulledge test [1]. The Interpolation is performed using the scipy.interpolate.interp1d() function.

  • If the test statistic is greater than the critical value for \(\alpha=0.995\), the result is always “p > 0.995”.

  • If the test statistic is lower than the critical value for \(\alpha=0.005\), the result is always “p < 0.005”.

Warning

The estimated \(p_{value}\) may not be accurate as it is calculated using linear interpolation

References

[1] (1,2)

LOONEY, S. W.; GULLEDGE, T. R. Use of the Correlation Coefficient with Normal Probability Plots. The American Statistician, v. 39, n. 1, p. 75-79, fev. 1985.

Examples

>>> from normtest import looney_gulledge
>>> p_value = looney_gulledge._p_value(0.98538, 7)
>>> print(p_value)
0.8883750000000009