This document discusses applications of statistics in software engineering. It introduces a special issue that highlights papers applying statistical methods to solve software engineering problems and improve decision making. The issue includes papers on using statistical significance testing and Bayesian belief networks for risk management, using regression splines to understand factors affecting code inspection effectiveness, using Markov chains for reliability modeling, and applying clustering techniques for software partitioning and recovery. The document emphasizes that statistical analysis can help manage uncertainties in development, but challenges remain in collecting good data and integrating these methods into practice and education.