are better performed by humans and some are better performed by machines. Furthermore, human errors in management; the organization of data and information; or the misuse or abuse of items, may also contribute to unreliability. This is the core reason why high levels of reliability for complex systems can only be achieved by following a robust systems engineering process with proper planning and execution of the validation and verification tasks. This also includes careful organization of data and information sharing and creating a "reliability culture", in the same way that having a "safety culture" is paramount in the development of safety critical systems. Reliability prediction and improvement Reliability prediction combines: creation of a proper reliability model (see further on this page) estimation (and justification) of input parameters for this model (e.g. failure rates for a particular failure mode or event and the mean time to repair the system for a particular failure) estimation of output reliability parameters at system or part level (i.e. system availability or frequency of a particular functional failure) The emphasis on quantification and target setting (e.g. MTBF) might imply there is a limit to achievable reliability, however, there is no inherent limit and development of higher reliability does not need to be more costly. In addition, they argue that prediction of reliability from historic data can be very misleading, with comparisons only valid for identical designs, products, manufacturing processes, and maintenance with identical operating loads and usage environments. Even minor changes in any of these could have major effects on reliability. Furthermore, the most unreliable and important items (i.e. the most interesting candidates for a reliability investigation) are most likely to be modified and re-engineered since historical data was gathered, making the standard (re-active or pro-active) statistical methods and processes used in e.g. medical or insurance industries less effective. Another surprising – but logical – argument is that to be able to accurately predict reliability by testing, the exact mechanisms of failure must be known and therefore – in most cases – could be prevented! Following the incorrect route of trying to quantify and solve a complex reliability engineering problem in terms of MTBF or probability using an-incorrect – for example, the re-active – approach is referred to by Barnard as "Playing the Numbers Game" and is regarded as bad practice. For existing systems, it is arguable that any attempt by a responsible program to correct