Produced: Uphold the trustworthiness, quality and value of statistics and data used as evidence
OSR’s regulatory work revolves around the Code of Practice for Statistics (the Code), where we set the standards that producers of official statistics should commit to. The Code itself rests on three pillars: trustworthiness, quality, and value. To continue increasing our capability as a regulator, we are constantly seeking to grow our understanding of what these pillars mean and how they can be supported.
When creating the Code our perspective was informed by research, such as by the work of Onora O’Neill who has highlighted how for an organisation (or a statistic) to be trusted it must first be trustworthy. Since then, we have continued to engage with relevant research, such as the United Nations Economic Commission for Europe (UNECE) on its exploration of value and how the value of official statistics might be measured. Going forward, we intend to continue to consolidate and expand on this valuable research, drawing on knowledge both specific to the statistical system and more broadly. We want our advice, guidance, and regulatory work to be evidence-based and pragmatic, to best support statistics producers and users.
While we are open to learning about any research that will help us uphold the trustworthiness, quality and value of statistics and data used as evidence, there are also specific questions that we are seeking to answer:
- What influences perceived trustworthiness of statistics or data being used as evidence, and how can those across the statistical system increase it?
- To what degree do people trust official statistics or other data used as evidence, and how much do perceptions of trustworthiness influence this?
- How does the quality of statistics and communication of this quality influence public confidence in the statistics?
- How should the quality of statistics be conceived, measured, and communicated to support users in selecting statistics that are most appropriate for their needs?
- Building on the work of others, how should we understand and measure the value of statistics and data used as evidence, and what influences this?