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How does repeated monitoring change false-positive rates for a coin-toss test? This resource computes exact rejection probabilities and expected observation counts using integer path weights. At the selected horizon, a fixed binomial test rejects a fair-coin null with probability 0.04431304005703379, repeated binomial testing with probability 0.20205809786793333, and a likelihood-ratio test with probability 0.04088964341296718. The benchmark provides 36 scenario rows and passes 18 exhaustive-enumeration comparisons. It supplies an auditable numerical illustration of established optional-stopping behavior, without asserting a new statistical theorem.
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.