Recently pathogen counts in drinking and source waters were shown theoretically to have the discrete Weibull (DW) or closely related discrete growth distribution (DGD). The result was demonstrated versus nine short-term and three simulated long-term water quality datasets. These distributions are highly skewed such that available datasets seldom represent the rare but important high-count events, making estimation of the long-term mean difficult. In the current work the methods, and data record length, required to assess long-term mean microbial count were evaluated by simulation of representative DW and DGD waterborne pathogen count distributions. Also, microbial count data were analyzed spectrally for correlation and cycles. In general, longer data records were required for more highly skewed distributions, conceptually associated with more highly treated water. In particular, 500–1,000 random samples were required for reliable assessment of the population mean ±10%, though 50–100 samples produced an estimate within one log (45%) below. A simple correlated first order model was shown to produce count series with 1/f signal, and such periodicity over many scales was shown in empirical microbial count data, for consideration in sampling. A tiered management strategy is recommended, including a plan for rapid response to unusual levels of routinely-monitored water quality indicators.