Arrange the results as a $2\times 2$-table and use chi2_contigency from SciPy in Python to obtain the correct $p$-value (here shown without continuity correction):

import numpy as np
from scipy.stats import chi2_contingency

obs = np.array([[8157, 8],[7906,10]])

g, p, dof, expctd = chi2_contingency(obs, correction = False)

p

0.59094761107842753

So the $p$-value is roughly $0.5909$.

Arrange the results as a $2\times 2$-table and use chi2_contigency from SciPy in Python to obtain the correct $p$-value (here shown without continuity correction):

import numpy as np
from scipy.stats import chi2_contingency, fisher_exact

obs = np.array([[8157, 8],[7906,10]])

g, p, dof, expctd = chi2_contingency(obs, correction = False)

p

0.59094761107842753

So the $p$-value is roughly $0.5909$.

A viable alternative would be to use Fisher's exact test. This can be done using fisher_exact from SciPy:

oddsr, p_fish = fisher_exact(obs)

oddsr

1.289685049329623

p_fish

0.64294290970149048

The odds ratio is $1.29$ with an associated $p$-value from Fisher's exact test of $0.643$.