The purpose of statistical timing simulation is to assess the impact of process variations on pattern delays. In this paper, we propose a novel hazard-aware statistical timing simulator. Our simulator characterizes timing hazards in terms of uncertainty windows whose widths are computed as random variables. Given a 2-pattern vector, the simulator estimates the transition times and uncertainty windows at each circuit output as two separate random variables. We demonstrate that this simulator achieves much higher accuracy and robustness than its predecessor. With this improved statistical timing simulator, we study its applications from three perspectives: (1) improving test effectiveness through pattern selection (2) enhancing defect detection by using multiple test frequencies (3) extending defect coverage for different voltages and temperatures. Experimental results are presented to demonstrate the benefits as well as the limitations of using the statistical timing simulator in the context of screening frequency-dependent defects