Energy efficiency is a growing concern in modern datacenters. As Internet services increasingly rely on MapReduce workloads to fuel their flagship businesses, there is a growing need for better MapReduce energy efficency evaluation mechanisms. We present a statistics-driven workload generation framework that distills summary statistics from production MapReduce traces and realistically reproduces representative workloads. These workloads help us evaluate design decisions with regard to scale, configuration, scheduling, and other issues. We use this framework to identify specific suggestions to improve MapReduce energy efficiency. Our key finding is that evaluations using trace-driven workloads reverse current design priorities in optimizing for data intensive synthetic jobs.