High profile cases like BP, Toyota, and Firestone bring into sharp relief the subject of engineering for reliability. As statisticians, we seem to have got everybody from ourselves, to scientists & engineers, to senior management and to regulatory authorities, comfortable with the idea of expressing reliability as a probability. Indeed, in media interviews, the BP CEO quoted a failure probability of “about 10-5” for the oil rig that exploded causing the spill. In his investigation into the 1986 Challenger disaster, when NASA management had quoted a similar probability for the reliability of the Space Shuttle, Richard Feynman said in his report into the accident “What is the cause of management’s fantastic faith in the machinery?” Probability measures for reliability may be appropriate for some fields of engineering, but I will introduce an information based definition that is better suited to many engineering situations (including automotive) where the probability definition simply can’t be measured. I will argue that the focus should be on evaluating the efficacy of counter measures for identified potential failure modes, and the statistical methods required to evaluate this efficacy are much different to those required in attempting to measure reliability through a probability.