Among statistical agencies there is no commonly accepted definition of data quality for official statistics. Statistical quality in the past might have been synonymous with accuracy, but today a consensus is emerging that quality is a much wider multidimensional concept. The most commonly accepted way of defining data quality is in terms of the broad notion of 'fitness for purpose'. Fitness for purpose encompasses not only the accuracy and reliability of statistical outputs but also other characteristics, such as relevance and timeliness, that determine how effectively statistical information can be used. While some aspects of quality can be assessed in a more or less objective way, an assessment of the wider concept of fitness for purpose is largely subjective as it also brings to account other factors including user views, the soundness of statistical practices and corporate culture, more generally, within the statistics agency. Quality is not absolute. There are a number of trade-offs in the various aspects of quality that have to be managed in consultation with users. It also has to be seen in the context of what is feasible in practice.