Analyzing failure data for repairable systems sounds straightforward—until you start asking the right questions. In this episode, Chris and Fred unpack a listener’s challenge: how to model reliability when failed components are repaired and returned to service. The discussion quickly moves beyond Weibull curves and into a more uncomfortable reality—maintenance itself often increases failure risk, at least in the short term. With limited data and multiple failure mechanisms at play, traditional statistical approaches can mislead more than they help. Instead, the focus shifts to understanding failure physics, questioning assumptions, and making practical decisions that actually improve reliability—not just model it.