# On the Public Understanding of – and Public Engagement With – Statistics: Reflections on the OU Statistics Group Conference on “Visualisation and Presentation in Statistics”

Last week I attended the OU Statistics conference on Visualisation and Presentation in Statistics (VIPS) (notes: here and here)

One of the things that struck me from conversations and some of the presentations was that statistics – and in particular public engagement around statistics – appears to be lagging science efforts in this area.

When I first moved to the OU as a lecturer a dozen or so years ago, I got involved with various activities that, at the time, were classed as “public understanding of science and technology”, though at the time the whole sci-comm area was in a state of flux and ideas were moving towards a focus on public *engagement* with science. As a member of the NESTA Crucible one year, I saw how there was also concern around engagement with science and technology policy, and how it could be moved “upstream”, to a point where dialogue with various publics could actually contribute to, and even influence, policy development.

(The NESTA Crucible experience significantly influenced my world view and was one of the most rewarding schemes I have ever been involved with…)

Since then, it seems to me that the school science curriculum has witnessed a similar change, with a move away from a focus purely on the basic science (and perhaps industrial applications?) to one that includes a consideration of socio-technical considerations (one might say, policy implications…)

At the VIPS event, one of the phrases that jumped out at me in at least one presentation (aside from repeated mentions to RSS…;-) talked about difficulties in promoting the *public understanding of statistics*. Ally this with the fact that the school maths curriculum seems *not* to have evolved so much, (“averages”, means and histogram still seem to be the focus?!) and I wonder: is statistics today where science was a decade or so ago?

The recent rhetoric around – and actual release of – “open public data” suggests that, as citizens and journalists, there is an increasing number of opportunities to hold governments and public bodies to account using evidentiary data and maybe also engage in data-driven (or at least data informed) policy formulation. With so much data out there, and so many possible ways of combining and interrogating it – so many possible different questions to ask and places to ask them – there are increasingly opportunities for informed amateurs to make a very real contribution (in the same way that amateur astronomers can make a real contribution to the recording and analysis of astronomical observations).

The growing instrumentation of our world also means that there is increasing amounts of data about ourselves that we can have access to in the form of personal data dashboards (for example, think of various social media/reputation tools, but also expect to see various tools appearing that allow you to mine your health/fitness, financial or shopping transaction data, for example). These dashboards will be visually rich, and designed to give at-a-glance overviews of the state of this, or that quantity or metric. But to get most from them, we will need to include more complex and powerful visualisation types, *and find a way of helping people learn how to “see” them, “read” them and interpret them*/

So to what extent do we need to engage with the “public understanding of statistics” as compared to the development of skills in the public *appreciation* of statistics and improvements in the way the public can engage with each other and with policy makers in discussions where statistics play a role? (Public engagement *in* statistics? Public engagement *with* statistics?)

Over the last few weeks, I’ve started trying to immerse myself in the world of statistical graphics, on the basis that our perceptual apparatus is pretty good at pattern detection and can help us get to grip with visually meaningful properties of distributions of data without us necessarily having to understand much in the way of formal statistics. (Of course, the visual apparatus can also be conned by misleading graphs and charts, which is where some semblance of critical understanding and, dare I say it, statistical literacy, comes in.)

My intuition is that it will be easier to develop a visual literacy in the reading and interpretation of charts (i.e. building on “folk statistical graphics/visual statistics”) than a widespread mathematical understanding of statistics. (I suspect that for most people, pie charts – and more recently ‘donut’ charts – as well as line graphs and simple bar charts are about the limit of what they are comfortable with, along with thematic maps (in particular, *choropleth* maps) and (in recent years again?) *proportional symbol maps*. I also know from asking even well informed audiences that awareness of more recently developed techniques, such as treemaps, are not widespread.)

At the moment, the infographics designers appear to be leading the charge into public consciousness of data-driven graphics, but as I’m finding out, the stats community has a wealth of visual techniques already to hand that are maybe “sounder” in terms of deriving visual representations that reflect statistical properties and concerns than the tricks the infographics crowd are using. (This is all just my anecdotal opinion, and not based in any formal research!)

Many infographics build on a common visual grammar (in the West, line charts up to the right increase over time; for area based charts, the bigger the area the more of something is being represented). But many infographics are also limited by the chart types we are all familiar with (line charts, bar charts, coloured maps…) Maybe the place to start is the stats community finding ways of introducing new-to-the-majority statistical graphs into the mainstream media along with a strong narrative to explain what is going on in those charts (and not necessarily so much discussion about the actual maths and stats…)?

The online community around R is both the best place to pose these questions and probably the best placed to reply. Suggest you post this to http://www.r-bloggers.com

Your final sentence wished for a new-to-the-majority statistical graphs with a strong narrative rather than maths and stats. Is OECD’s new Better Life Index http://oe.cd/bli the sort of thing you are wishing for?

@toby I hadn’t seen that OECD site – thanks for the link:-)

The current breakdowns are interesting (e.g. http://www.oecdbetterlifeindex.org/countries/greece/ ) in the way that text is used to explain and contextualise the vbar charts and each country’s position within a chart (which also invite the use to hover over different bars to see the neighbouring countries or extreme countries on each metric).

The Better Life index is very shiny, but I wonder: do users get much more out of it than blindly and wildly changing the values in the right hand panel?

In his presentation at the OU stats conference ( http://blog.ouseful.info/2011/05/18/quick-summary-of-opening-session-of-visualisation-and-presentation-in-statistics/ ), Michael Blastland gave a very open account about some of the interactive visualisations published by the BBC (links in the above report), admitting that in many cases they just didn’t work. For sure they were shiny and offered open ended exploration possibilities, but in many cases the user arrived with no context that allowed them to play meaningfully, or in a directed fashion?

Part of the point of these interactives is surely related to trying to help users develop or understand the model the interactive represents, and then test their understanding/appreciation of the model against the interactive?

(I think I need to start reading philosophy of science books again!)

I think the overall point about engagement (as opposed to understanding) is a good one, and we statisticians should think in those terms, but the situation in statistics is different from in science. Putting it far too crudely (and arguing against some things I said at VIPS!), statistics largely consists of methodologies for exploring questions that aren’t themselves really part of statistics, and so it’s even more important than in science to persuade people that engaging with statistics is a way of getting to things that they really want to know or do, and which might well not look very statistical at all. This links to what Michael Blastland was saying; one way of looking at that is that the shiny tools are no use without having got people interested in the questions you can answer with them. The distance between what the statistics can at face value tell you, and the things you actually want to know, can be pretty great — bridgeable, but it’s a long bridge often. (And here I’m talking about /any/ sort of statistical information or tools, really, including the new kinds of visualisations.) Other issues are that people already /do/ engage with statistics in some areas (e.g. sport) on a major scale, so there’s important stuff to build on in further engagement — that’s good, but brings its own challenges. And that the nature of “citizen statistics” looks (so far anyway) very different from “citizen science”. The science tends to be stuff that’s led and directed by professional scientists, and the citizens help with collecting data and making comments and looking for patterns in a directed way, and that sort of thing. “Citizen statistics” in the sense of using (and understanding) public data and public tools is different, in that, in principle (and sometimes in practice too) it really is done by individual “citizens” or groups of them, to deal with their own concerns and answer their own questions, and the input from the professional statistician or data scientist (or whatever one wants to call them) can be limited to help them use, appreciate, engage with the tolls and data sources rather than setting the agenda.

@kevin I think making the distinction between engagement with science vs engagement with statistics is an interesting one and is something I will ponder. I’m also considering replacing the word “statistics” with “tool” or “technology” when developing my ideas/arguments in this area, and then as a sideline consider what it would take for “the public” to see stats in the same way. (As I think you hint at too, there may be a mismatch between how different audiences think of statistics and what statisticians do, which just adds clutter and confusion to the engagement exercise.)

I never thought it would be stats that would rekindle my interest in the practice of science and technology engagement/communication!