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How do I test hypotheses in Python?

Ideally, I would like to find the value of P. I am coming from a more statistical background and am fairly new to Python. Are there any packages that will allow me to do this? I am following the book "Data Science from Scratch" and kind of stuck with testing and hypothesis.

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The SciPy package has a whole module with lots of statistics, including hypothesis tests and built-in distribution functions: scipy.stats

For example, you can check if a random sample is normally distributed using the Kolmogorov-Smirnov test:



import numpy as np
from scipy.stats import norm, pareto, kstest

n = 1000
sample_norm = norm.rvs(size=1000)  # generate normally distributed random sample
sample_pareto = pareto.rvs(1.0, size=1000)  # sample from some other distribution for comparison

d_norm, p_norm = kstest(sample_norm, norm.cdf)  # test if the sample_norm is distributed normally (correct hypothesis)
d_pareto, p_pareto = kstest(sample_pareto, norm.cdf)  # test if the sample_pareto is distributed normally (false hypothesis)

print('Statistic values: %.4f, %.4f' % (d_norm, d_pareto))
print('P-values: %.4f, %.4f' % (p_norm, p_pareto))

As you can see, it kstest

returns statistic value and p value. norm.cdf

denotes the cumulative distribution function of a normal random variable.

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