There’s more to statistical communication than avoiding truncated axes…

In our latest blog, DG for Regulation Ed Humpherson and Head of Casework, Elise Baseley talk about the importance of communication to make statistics accessible and meaningful.

There’s more to statistical communication than avoiding truncated axes. It’s about making statistics accessible, meaningful, perhaps even enjoyable, for people.

At the Office for Statistics Regulation, we’ve recently commented on data visualisations that are misleading, or potentially misleading. Our role in calling out these problems remains important. But we’ve recognised the need to go much deeper into what makes communication helpful and accessible.

Communication of statistics, focusing on accessibility, was a big theme of the recent UKSA strategy midpoint event that I participated in a couple of weeks ago. The event was great and had some brilliant speakers including: Laura Gilbert, head of No 10 Downing Street’s data science team; Ming Tang, chief data officer for NHS England; and Tim Harford, the journalist and broadcaster. In different ways, they all emphasised the need to make statistics relevant and accessible for people – both people who are users or potential users of statistics, and for people who want a career in statistics and data.

The event was summed up very neatly in Tim Harford’s advice to “think like a 14 year old”, reflecting on the success of the Australian Covid dashboard.’ Tim was highlighting that the Australian version of the Covid dashboard turned out to be developed by three 14-year-olds. Apparently, they outed themselves once they were vaccinated and appeared in the data. The three teenagers had been experimenting with displaying the data and it turned out to be a massive success as it was communicated so simply.

While the event gave me a renewed enthusiasm for the importance of statistical communication, I found it hard to escape the feeling that OSR could be doing more to drive improvement in this space. Statistics must be communicated in a way that is easy to understand. The teams who produce statistics should be willing to explain what they mean – and what they don’t mean: recognition of uncertainty is so important. And as the work of the Winton Centre at the University of Cambridge has shown, being honest about uncertainty doesn’t seem to damage trustworthiness at all.

As I said at the event, we can’t do it alone: it will require us to build partnerships with a wide range of other organisations who care about the public’s engagement with statistics.

Of course, we do a lot of work already on this. I’ve already mentioned our interventions on weaknesses in the use of visualisations. These are important issues and are recognised as such by communicators in Government. After we’ve intervened, the relevant teams have recognised the issues and put in place new practices to prevent their recurrence. We’re also working with the Analysis Function to reiterate good practice.

But we also know that these interventions are not enough. Instead of intervening when risky visualisations are produced, we need to be much more upstream: understanding what supports public understanding, how statistics should be communicated, and how to identify what matters to a wide range of public audiences.

This is where our review of statistical literacy comes in. We commissioned it because we get frustrated by the attitude that the problem with statistics is that the public doesn’t have the expertise to understand them. This always strikes us as being a bit exclusive, perhaps even arrogant. And maybe not even true – there is evidence from the pandemic that people are both interested in and well equipped to understand well-presented statistics. Instead, then, of focusing on weaknesses in other people’s statistical literacy, our review says producers should regard it is all a matter of communication.

So: this will be one of our key priorities for the year that’s coming. We know there’s more to supporting good statistical communication than stamping out truncated axes. As Tim Harford’s example of the Australian Covid dashboard demonstrates, sometimes we just need to approach the problem through the eyes of an inquiring citizen.