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Table B.1.

Statistical test results (p-values) comparing FSRQ and RQ NTD distributions for the full samples and different NTD regimes.

Sample ECDF Wasserstein Std. mean diff. Stddev ratio High moment diff.
Full Sample
K-S 1.426 × 10−9 1.725 × 10−9 1.426 × 10−9 1.614 × 10−9 1.582 × 10−9
A-D < 1 × 10−4 < 1 × 10−4 < 1 × 10−4 < 1 × 10−4 < 1 × 10−4
MWU 2.960 × 10−6 2.470 × 10−6 2.923 × 10−6 1.201 × 10−6 4.716 × 10−5

Disk-Dominated
K-S 0.198 0.198 0.240 0.240 0.084
A-D 0.259 0.358 0.269 0.343 0.139
MWU 0.350 0.389 0.364 0.495 0.143

Jet-Dominated
K-S 3.715 × 10−11 1.223 × 10−10 8.255 × 10−11 3.076 × 10−12 3.710 × 10−11
A-D < 1 × 10−4 < 1 × 10−4 < 1 × 10−4 < 1 × 10−4 < 1 × 10−4
MWU 1.577 × 10−13 1.197 × 10−13 1.197 × 10−13 3.061 × 10−15 1.706 × 10−13

NTD)<1
K-S 1.080 × 10−8 2.985 × 10−8 4.665 × 10−9 3.737 × 10−9 3.187 × 10−9
A-D < 1 × 10−4 < 1 × 10−4 < 1 × 10−4 < 1 × 10−4 < 1 × 10−4
MWU 5.888 × 10−12 1.096 × 10−11 7.598 × 10−12 2.358 × 10−12 3.566 × 10−13

Note 1: The comparisons are based on different metrics used to select the optimal bootstrap subsample: Empirical Cumulative Distribution Function (ECDF, column 2), Wasserstein distance (column 3), standardized mean difference (column 4), standard deviation ratio (column 5), and higher moment differences (column 6). p-values lower than 0.05 indicate statistically significant differences. Note 2: P-values reported as < 1 × 10−4 for the Anderson-Darling (A-D) test indicate that the calculated value is below the lower precision limit of the scipy.stats.anderson_ksamp function in Python.

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