In an earlier statistics roundtable column, the authors described how the conclusions you can draw from statistical analysis of limited life data can be bolstered by appropriately incorporating engineering knowledge and experience into the analysis. Now, let them demonstrate how Bayesian methods can be used as an alternative in these evaluations. The preceding analyses provided useful insights. Management, however, wanted a more definitive analysis with a single quantitative estimate of reliability and the associated statistical uncertainty. There has been a substantial increase hi the use of Bayesian methods during the past 20 years. Today, most of these applications use Monte Carlo simulations to generate a sample from the desired joint posterior distribution. Traditional methods require various assumptions -- for example, a Weibull distribution for time to failure and representative samples and test environments -- that demand careful examination. Bayesian methods require the further assumption era prior distribution based on existing knowledge.