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In frequentist hypothesis testing, the $p$-value is the probability of a result as extreme (or more) than the observed result, under the assumption that the null hypothesis is true.

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How to calculate the p-value of a test, that checked for a binary property?

It looks like you could treat this as a 2x2 contingency table and use Fisher's Exact Test. So, I'd recommend you use scipy.stats.chi2_contingency instead of chisquare.
sefrabusle's user avatar