  Possible parameter values in a random sampling model are shown by definition
to have uniform base-rate prior probabilities. This allows a frequentist
posterior probability distribution to be calculated for such possible parameter
values conditional solely on actual study observations. If the likelihood
probability distribution of a random selection is modelled with a symmetrical
continuous function then the frequentist posterior probability of something
equal to or more extreme than the null hypothesis will be equal to the P-value;
otherwise the P value would be an approximation. An idealistic probability of
replication based on an assumption of perfect study methodological
reproducibility can be used as the upper bound of a realistic probability of
replication that may be affected by various confounding factors. Bayesian
distributions can be combined with these frequentist distributions. The
idealistic frequentist posterior probability of replication may be easier than
the P-value for non-statisticians to understand and to interpret.
