Simulated and unbiased reliability sampling plans

Statistical estimation and hypothesis testing are two ways in which the data of a sample can be made to yield information concerning the parameters of the population from which the sample is drawn. The most important applications of these two ways is the acceptance sampling methods used in industrial quality control, systems reliability and failure detection. The use of these sampling methods is connected with the risk of unnecessary rejection of satisfactory lots and the risk of acceptance of lots with defective units, these decisions can occur when selecting biased samples containing most defective units or no defective units despite the fact that the lot has the contrary. The aim of this research is to make this kind of decision an improbable or impossible event. The parameters and techniques determining the sampling methods must be correctly chosen. The formulation of an optimised statistical model of these problems is the basic condition of obtaining objective results. By the essence of statistical simulation the process of functioning of the complex system was used to represent a mathematically formulated model which was isomorphic in all essential aspects of the research objectives. This model was repeatedly tested to determine the required statistical characteristics, based on the complex stochastic process.