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For quality assessment and decision making, metrics values are aggregated to characteristics and ultimately to quality scores. Aggregation has often been problematic as quality models do not provide the semantics of aggregation. This makes it hard to formally reason about metrics, characteristics, and quality. We argue that aggregation needs to be interpretable and mathematically well defined in order to assess, to compare, and to improve quality. To address this challenge, we propose a probabilistic approach to aggregation and define quality scores based on joint distributions of absolute metrics values. To evaluate the proposed approach and its implementation under realistic conditions, we conduct empirical studies on <jats:italic>bug prediction<\/jats:italic> of ca. 5000 software classes, <jats:italic>maintainability<\/jats:italic> of ca. 15000 open-source software systems, and on the <jats:italic>information quality<\/jats:italic> of ca. 100000 real-world technical documents. 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