  The paper addresses general aspects of experimental data analysis, dealing
with the separation of ``signal vs. background''. It consists of two parts.
  Part I is a tutorial on statistical event classification, Bayesian inference,
and test optimization. Aspects of the base data sample if being created by
Poisson processes are discussed, and a method for estimating the unknown
numbers of signal and background events is presented. Data quality of the
selected events sample is assessed by the expected purity and background
contamination.
  Part II contains a rigorous statistical analysis of the methods discussed in
Part I. Both Bayesian and frequentist estimators of the unknown
signal/background content are investigated. The estimates and their stochastic
uncertainties are calculated for various conjugate priors in the Bayesian case,
and for three choices of the virtual parent population in the frequentist case.
