Normality tests are based on skewness and kurtosis.
The normaltest() function returns the p-value for the null hypothesis:
“X comes from a normal distribution.”
Skewness measures the symmetry of data.
For normal distributions, the skewness is 0.
A negative skew indicates the data is skewed to the left, while a positive skew indicates the data is skewed to the right.
Kurtosis measures whether the data is heavy-tailed or lightly-tailed compared to a normal distribution.
Positive kurtosis indicates heavy tails, while negative kurtosis indicates light tails.
Calculate the skewness and kurtosis of the values in an array.
| import numpy as np from scipy.stats import skew, kurtosis v = np.random.normal(size=100) print(skew(v)) print(kurtosis(v)) |
|
0.11168446328610283 -0.1879320563260931 |
Determine if the data follows a normal distribution.
| import numpy as np from scipy.stats import normaltest v = np.random.normal(size=100) print(normaltest(v)) |
|
NormaltestResult(statistic=4.4783745697002848, pvalue=0.10654505998635538) |
