To provide designers with deeper information about their system's fielded performance, we are developing and evaluating a new class of performance analyses. Starting with some designer-defined system space, our techniques recast certain performance analyses as large-scale statistically-designed experiments that are performed collaboratively, and in parallel, on a wide variety of fielded resources. The experimental analysis identifies a subset of variability dimensions that most affect performance. Designers can then focus on this much smaller system space when reasoning about certain future design and development decisions.  This approach isn't meant to replace all in-house performance analyses.  In particular, since we do not fully control the fielded resources on which the performance measurement tasks run, our approach can't provide completely noise free measurements. Instead, we seek to identify general trends and detect relative performance differences that will be experienced by eventual end users. Some of our recent work is described in: Cemal Yilmaz, Adam Porter, Arvind Krishna, Atif Memon, Douglas C. Schmidt, Aniruddha Gokhale, and Bala Natarajan, Reliable Effects Screening: A Distributed Continuous Quality Assurance Process for Monitoring Performance Degradation in Evolving Software Systems. (submitted to IEEE Transactions on Software Engineering, Dec. 2005 Alan F. Karr and Adam A. Porter, Distributed Performance Testing using Statistical Modeling, 1st International Workshop on Advances in Model-Based Software Testing, St Louis, Mo, May 2005.