Exponential growth in data collection is creating significant challenges for data storage and analytics latency.Approximate Query Processing (AQP) has long been touted as a solution for accelerating analytics on large datasets, however, there is still room for improvement across all key performance criteria. In this paper, we propose a novel histogram-based data synopsis called PairwiseHist that uses recursive hypothesis testing to ensure accurate histograms and can be built on top of data compressed using Generalized Deduplication (GD). We thus show that GD data compression can contribute to AQP. Compared to state-of-the-art AQP approaches, PairwiseHist achieves better performance across all key metrics, including 2.6$ \times $ higher accuracy, 3.5$ \times $ lower latency, 24$ \times $ smaller synopses and 1.5--4$ \times $ faster construction time.