When I joined OSR as a placement student last September, the Code of Practice for Statistics was unknown territory. It certainly sounded official and important. Was it password protected? Would I need to decipher something or solve a puzzle to get in?

It soon became clear to me that this elusive ‘Code’ was at the heart of everything I would be doing at OSR. Not wanting to remain in the dark any longer, I dutifully dragged it to the front of my bookmarks bar and began to familiarise myself with its contents. (Thankfully no complicated code-cracking required).

The Trustworthiness and Value pillars appeared to be pretty straightforward. Yet, something about the Quality pillar didn’t seem quite so inviting. It sounded like the technical, ‘stats-y stuff’ pillar, that my degree background in economics and politics would surely leave me ill-equipped to understand.

*Spoiler alert* I was wrong.

It turns out that ensuring statistics are the highest quality they can be, isn’t as complicated and technical as I once feared. Quality simply means that statistics do what they set out to do and, crucially, that the best possible methods and sources are used to achieve that.

There are lots of ways that statistics producers can meet these aims. For example, quality can be achieved through collaboration. This can be with statistical experts and other producers, to arrive at the best methods for producing data. It can also be with the individuals and organisations involved in the various different stages of the production process – from collecting, to recording, supplying, linking, and publishing. Collaborating in these ways not only helps to ensure that statistics are accurate and reliable, but also that they are consistent over time and comparable across countries too.

There are lots of other important-sounding documents like our Code of Practice that set out national or international best practise and recognised standards and definitions for producing statistics and data such as the GSS harmonisation standards and the Quality Assurance Framework for the European Statistics System. These also help producers ensure that their statistics and data meet the highest possible standards of quality.

Quality is not only important at the producer-end of the equation, but at the user-end too. It is vital that producers are transparent with their users about how they are ensuring the quality of their statistics. This means telling users about the steps they take to achieve this, and being clear with them about the strengths and limitations of the statistics with respect to the various different ways in which they could be used.

For an indication of just how important quality is, the Quality Review of HMRC Statistics we conducted last year is a prime example. After identifying an error in its published Corporation Tax receipt statistics, HMRC asked us to assess its approach to managing quality and risk in the production of its official statistics. With the Code as our guide, we were able to review HMRC’s existing processes and identify potential improvements that could be made to reduce the risk of statistical errors in the future.

This is just one example of how high-quality data fulfils our vision of statistics that serve the public good. We have found many others across our work and we continue to support producers to consider quality when producing statistics. Last year, we published new guidance for producers on thinking about quality, which was inspired by the HMRC review and the questions we asked.

If you’re interested in finding out more about Quality and the other pillars of our Code, check out the Code of Practice website. I promise it’s not as scary or elusive as it sounds…


Did you know we have case studies on our Code website too? Here are some of our examples that highlight examples of good practice in applying the quality pillar of the Code.

  • Q1 – Ensuring source data is appropriate for intended uses
  • Q2 – Developing harmonised national indicators of loneliness
  • Q3 – Improving quality assurance and its communication to aid user interpretation