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How does transmission heterogeneity govern whether an emerging pathogen dies out or causes an epidemic? In negative binomial branching processes at reproduction number 2.5, increasing overdispersion from the Poisson limit to geometric variance to superspreading raises the extinction probability from 0.107355 to 0.4 to 0.860685. Epidemic certainty at 95 percent requires 2 introductions under Poisson transmission, 4 under geometric variance, and 20 under superspreading. Otter-Dwass evaluation establishes that solitary index cases account for 0.721943 extinction probability, or 83.88 percent of extinctions. Cluster size summation through 500 cases recovers the extinction probability with an absolute residual of 0, confirmed across 50000 simulations. Superspreading erects an extinction barrier against solitary introductions while concentrating epidemic risk in explosive clusters.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: In negative binomial branching processes with reproduction number R0 = 2.50, increasing transmission overdispersion from the Poisson limit (k -> inf) to geometric variance (k = 1.00) to SARS-like superspreading (k = 0.10) increases the single-case extinction probability from 0.107355 to 0.400000 to 0.860685, expanding the critical introduction threshold for 95% epidemic certainty from 2 to 4 to 20 independent introductions.
• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: For SARS-like superspreading dynamics (R0 = 2.50, k = 0.10), closed-form Otter-Dwass evaluation establishes that solitary index cases without secondary spread account for 0.721943 extinction probability (83.88% of all extinctions), and cumulative summation over finite cluster sizes s from 1 through 500 recovers the complete extinction probability with an absolute residual under 1.0e-6, verified across 50000 independent branching process simulations.

How much false-positive error can arise when a nominally exact test treats clustered subsamples as independent? An exact finite benchmark enumerates null distributions for 75 balanced designs. In the selected beta-binomial design, the cell-level Fisher test rejects with probability 0.21567065919813758, compared with 0.0436374106894531 for a test using the known clustered null. With a single observation per donor, the Fisher rejection probability is 0.021484375. These are exact probabilities conditional on the specified toy model. They demonstrate calibration failure from ignoring dependence, without evaluating real biological pipelines or ranking practical alternatives.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: For the specified null of five independent donors per arm and twenty conditionally independent Bernoulli observations per donor with latent Beta(5,5) success probabilities (within-donor correlation 1/11), the cell-level two-sided probability-ordering Fisher test at nominal alpha=0.05 rejects with exact-model probability 0.215670659198, versus 0.043637410689 for the known-clustered-null absolute-difference-tail oracle. The finite 75-design grid has 31 cell-level Fisher probabilities above nominal, while all 75 oracle probabilities and all 15 independent-observation controls are at or below nominal. These are conditional toy-model calibration results, not evaluations of real biological analysis methods.