Automated Topic Analysis of the Voynich Manuscript

[quimqu]

(*) but this does not remove my doubts about the p-values, they're really too small.

Well,I used scipy function:

scipy.stats.chi2_contingency 

It performs a Chi-square test of independence on a contingency table.

Input: a 2D table of observed frequencies (e.g., topics × languages, or topics × scribal hands).

Null hypothesis (H₀): the two categorical variables are independent.

Output:

chi2 → the Chi-square statistic

p → the p-value

dof → degrees of freedom = (rows − 1) × (columns − 1)

expected → the expected counts under H₀

Where I gave in my post p.

This is the full output with the 4 outputs per table:

Topic vs language:
 Chi2ContingencyResult(statistic=177.25623014793612, pvalue=8.693479383813543e-33, dof=10, expected_freq=array(
[[23.93714927, 19.06285073],
[50.1010101 , 39.8989899 ],
[65.68799102, 52.31200898],
[51.21436588, 40.78563412],
[36.18406285, 28.81593715],
[39.52413019, 31.47586981],
[30.06060606, 23.93939394],
[48.98765432, 39.01234568],
[41.75084175, 33.24915825],
[46.20426487, 36.79573513],
[62.34792368, 49.65207632]]))

Ttopic vs hand: Chi2ContingencyResult(statistic=689.3808658898682, pvalue=2.8408917882824174e-119, dof=40, expected_freq=array(
[[ 7.335578  , 16.02244669,  2.41301908, 16.60157127,  0.62738496],
[15.35353535, 33.53535354,  5.05050505, 34.74747475,  1.31313131],
[20.1301908 , 43.96857464,  6.62177329, 45.55780022,  1.72166105],
[15.69472503, 34.28058361,  5.1627385 , 35.51964085,  1.34231201],
[11.08866442, 24.21997755,  3.64758698, 25.09539843,  0.94837262],
[12.11223345, 26.45566779,  3.98428732, 27.41189675,  1.0359147 ],
[ 9.21212121, 20.12121212,  3.03030303, 20.84848485,  0.78787879],
[15.01234568, 32.79012346,  4.9382716 , 33.97530864,  1.28395062],
[12.79461279, 27.94612795,  4.20875421, 28.95622896,  1.09427609],
[14.15937149, 30.92704826,  4.65768799, 32.04489338,  1.21099888],
[19.10662177, 41.7328844 ,  6.28507295, 43.24130191,  1.63411897]]
))