When performing statistical analyses on PCP run-life data, one can easily reach incorrect conclusions if subtle but important issues are not fully understood. This paper illustrates some of these issues using examples from analysis work conducted recently with a relatively large and complete set of data collected from several large operators. Issues examined include misinterpretation of single-variable correlation results and run-life measures. It is impractical to design and conduct lab or field experiments to investigate the effect of individual variables on PCP run-life. On the other hand, with normal field data, it can be difficult to isolate the effects of individual variables. The assumption of “all else being equal” rarely applies to field data. Additionally, different run-life measures can give different results at different times in the life of a field, even when the underlying system reliability has not changed. Awareness of these issues is essential to all companies that collect and analyze PCP failure and run-life information for the purpose of evaluating and improving PCP system performance.