eelbrain.test.lilliefors
- eelbrain.test.lilliefors(data, formatted=False, **kwargs)
Lilliefors’ test for normal distribution
The Lilliefors test is an adaptation of the Kolmogorov-Smirnov test. It is used to test the null hypothesis that data come from a normally distributed population, when the null hypothesis does not specify which normal distribution, i.e. does not specify the expected value and variance.
- Parameters
data (array_like) –
formatted (bool) – Return a single string with the results instead of the numbers.
kwargs – All keyword arguments are forwarded to
scipy.stats.kstest()
.
- Returns
D (float) – The D-value of the Kolmogorov-Smirnov Test
p_estimate (float) – The approximate p value according to Dallal and Wilkinson (1986). Requires minimal sample size of 5. p is reasonably accurate only when it is <= .1 (cf. Dallal and Wilkens).
Notes
Uses the scipy.stats.kstest implementation of the Kolmogorov-Smirnov test.
References
- Dallal, G. E. and Wilkinson, L. (1986). An Analytic Approximation to the
Distribution of Lilliefors’s Test Statistic for Normality. The American Statistician, 40(4), 294–296.
- Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov Test for Normality
with Mean and Variance Unknown. Journal of the American Statistical Association, 62(318), 399–402.