What is Normality Tests explain with example

This recipe explains what is Normality Tests explain with example

Recipe Objective - What is Normality Tests explain with example?

This function tests the null hypothesis and returns the p-value for the null hypothesis.

Normality tests are based upon the skew and kurtosis to produce an omnibus test of normality.

Learn About the Application of ARCH and GARCH models in Real-World

Skew:

Skew is the measure of symmetry in data.

Skew value is 0, for normal distributions.

If the value is negative, it means the data is skewed left.

If the value is positive it means the data is skewed right.

Kurtosis:

Kurtosis is the measure of whether the data is heavy or lightly tailed to a normal distribution.

Positive kurtosis means heavy tailed.

Negative kurtosis means lightly tailed.

Example:- Find skew and Kurtosis

import numpy as np
from scipy.stats import skew, kurtosis

val = np.random.normal(size=150)

print(skew(val))
print(kurtosis(val))

Example:- Find normal test value.

import numpy as np
from scipy.stats import normaltest

val1 = np.random.normal(size=150)

print(normaltest(val1))

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