Sequential life testing with underlying normal and Weibull sampling distributions

Abstract

The sequential life testing approach gathers sample information only until there is enough to allow a decision with a desirable degree of cofildence. The sample size is a random variable and is determined by the result of the analysis of the observed data, It happens that even with the use of a sequential life testing approach, sometimes the number of items necessary to reach a decision about accepting or rejecting a null hypothesis is quite large, as shown by De Souza [1]. In situations like that, the development of a truncation mechanism is essential to guarantee the major advantage of using sequential life testing; that is, small sample sizes. In this work, we will develop a sequential life testing approach in which the underlying sampling distributions are the normal and the Weibull models, We will use the two underlying models to analyze a life testing situation, comparing the results obtained from both. We will also develop a truncation mechanism for the Weibull and Normal models. We will provide rules to truncate a sequential life testing situation making one of the two possible decisions at the moment of truncation; that is, acceptor reject the null hypothesis HO, An example will develop the proposed truncated sequential life testing approach for the Weibull and Normal models.