Two distinct Bayesian methodologies are developed and compared for inference on gamma scale parameters in one and two population problems. Both approaches permit concomitant variables and censored observations in the exponential case. The first approach, based on the use of natural-conjugate prior distributions, generalizes and harmonizes with the tradiational frequentist analysis in terms of chi2 and F distributions. The second method is based on non-continuous-type extensions of the natural conjugate priors, and involves the use of Bayes factors for sharply defined hypotheses. The two methods appear to conflict in hypothesis inference problems and to harmonize in interval estimation problems. Inferences from the two methods are compared for survival data from a Hodgkin's disease therapy trial.