In our regulatory work, when people talk to us about statistical literacy it is often in the context of it being something in which the public has a deficit. For example, ‘statistical literacy’ may be cited to us as a factor in a general discussion on why the public has a poor understanding of economic statistics.
But is it a deficit that can or needs to be addressed and – more importantly – what actually is statistical literacy?
To help answer these questions, as well as looking across our regulatory work and talking to other researchers, we commissioned a review of published research on this topic area. The review was carried out by Jessica McMaster, via a Cambridge Grand Challenges Internship, in the period March to September 2022.
This think-piece and the accompanying research review are our first publications specifically on this topic area. We hope that this evidence base will provide a useful resource for others who are working in this area.
What is statistical literacy?
Though the concept of statistical literacy has been discussed and researched for many decades, we found no consensus on what it means. Instead the term is being applied in different, often unrelated, contexts. The two most common contexts are education (for example, in the context of a school or university setting), and adults as data consumers (for example, in the context of explaining the ability of an adult to form a judgement when presented with a statistics).
Though context is one factor in the different definitions for statistical literacy, we found that there are other common components. These include foundational abilities – such as numeracy and overall general literacy, knowledge of statistical concepts, and an ability to critically evaluate statistical information.
As there is no common understanding of statistical literacy, we considered whether the use of the term is helpful, or whether instead it may be hindering action to address specific issues affecting the public’s ability to understand statistics.
We concluded that statistical literacy can be a useful term, but that it should be used consciously – anyone using the term should define what they mean by statistical literacy, considering the context that the term is being applied and the factors that they consider important in that context.
What is the general public’s level of statistical literacy?
Given that the concept of statistical literacy varies, it should come as no surprise that when carrying out this review we did not find a definitive measure of the general public’s statistical literacy.
What we did find was wide variability across the general public in the skills and abilities that are linked to statistical literacy. Our review highlights that a substantial proportion of the population display basic levels of foundational skills and statistical knowledge, and that skill level is influenced by demographic factors such as age, gender, education and socioeconomic status.
Given this, we think that it is important that statistical literacy is not viewed as a deficit that needs to be fixed, but instead as something that is varied and dependant on the context of the statistics and factors that are important in that context.
Therefore, rather than address deficits in skills or abilities, we recommend that producers of statistics focus on how best to publish and communicate statistics that can be understood by audiences with varying skill levels and abilities.
How should statistics be communicated?
Our review identified a number of areas where there is good evidence on how best to communicate statistics to non-specialist audiences. This evidence aligns not only with the principles of the Code of Practice for Statistics, but also with what we understand others have found. We hope the evidence in our review will help to reinforce and support current and future wider work on this topic.
Our review found good evidence to endorse existing features of best practice in communicating statistics, in the following areas:
Target audience: Our evidence endorses the widely recognised importance of understanding audiences. The evidence highlights that the best approach to communicating information (including data visualisations) can vary substantially depending on the characteristics of the audience for the statistics. Considering the target audience’s characteristics is, therefore, an important factor when designing communication materials.
Contextual information: Contextual information helps audiences to understand the significance of the statistics. Our evidence highlights the importance of providing narrative aids, and also that providing statistical context can help to establish trust in the statistics. Again, this supports and reflects existing notions of best practice.
Establishing trust: As well as providing context, we found evidence that highlighting the independent nature of the statistical body and, when needed, providing sufficient information so that the reasons for unexpected result are understood, can increase trust in the statistics. This finding aligns with the Code of Practice for Statistics, which includes Trustworthiness as one of its three pillars.
Language: In the statistical system, statistics producers recognise that they should aim for simple easy to understand language. We found evidence to endorse this recognition – in particular, that, when used, the level of technical language should be dictated by the intended target audience.
Format and framing of statistical information: We found evidence that different formats (e.g. probability, percentage or natural frequency) and/or framing (e.g. positive or negative) in wording can lead to unintended bias or affect perceptions of the statistics and both need to be considered. This finding is probably the one which is least widely recognised in current best practice in official statistics, and we consider it is an area that would benefit from further thinking.
Communicating uncertainty: Communicating uncertainty is important and may need to be tailored dependent on the information needs and interest levels of the audience. This topic is a particular focus area for OSR. In December 2022 we published a report that looks at how uncertainty is currently being communicated in official statistics, the current guidance that is available and our views on how communication of it can be improved.
We will be continuing our work in 2023/24 under our business priority ‘champion the effective communication of statistics to support society’s key information needs’. Included in this is our continued research focused on understanding what statistics serving the public good means in practice.
We have existing links with a number of researchers and organisations working on how to communicate statistics and are very open to working with others who have an interest in this topic.
Please get in touch with us at firstname.lastname@example.org if you would like to discuss any aspect of this think piece or our review with us.