Since new product designs have little field data available a correlation between field and accelerated test life cannot be made. However, a step partially accelerated life test approach where samples are tested under normal conditions for a time and then run to failure on an accelerated test can be used to estimate the statistical model parameters. This thesis developed the maximum likelihood parameter estimates for a step partially accelerated life test based on a Weibull distribution model for a hypothetical automotive component. Using a Monte Carlo approach with type-II censoring, the effect of sample size and length of sampling period used on the variability of the estimated parameters was examined. A smaller sampling period and small sizes lead to significant variability, which decreased as the sampling period and sample size increased. Use of a partitioned sample did not lead to an improvement in the variability of the estimates.