Misuse of statistics: What makes a statistic or data vulnerable to misuse, in what contexts is this more likely (including who is most susceptible to this and why), and how is this best addressed? What are the impacts when statistics or data are misused, how does misinformation propagate? How can public confidence in statistics or data be maintained in the face of misuse?
Background
When a statistics producer releases a statistic or other data, it does not exist in a vacuum. People may access the statistic, try to understand its insights, use it, form judgements based on what they have learnt, and ultimately make decisions and take certain actions. Statistics are central to public debate – in the media and in policy decisions. Consequently, how a statistic is accessed and used is as important as its production for determining whether it serves the public good.
OSR has already commissioned some research into how statistics are understood by the public, such as Jessica McMaster’s review on statistical literacy. However, we want to further enrich our understanding of consumption of statistics, including how statistics are used by individuals and organisations. We have greater knowledge in some areas, for example we have a programme of work on Analytical Leadership in government which (in part) seeks to uphold the integrity of analytical evidence and support robust evidence in policy development, but we are seeking a wider and more detailed picture of the role of evidence in decision-making by a range of users.
A deeper understanding of the role of statistics in public debate better enables its protection and promotion. However, as a regulator we must also understand the threats to the role of statistics. Whilst we have investigated misinformation, and we intervene directly in instances of misuse, this is a fast-changing environment and additional research is always welcome.
Next steps
If you would like to share evidence or collaborate with us on any of these areas, please contact us at [email protected].
Source
This question was published as part of the set of ARIs in this document:
Used Office for Statistics Regulations Areas of Research Interest Office for Statistics Regulation
Related UKRI funded projects
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Understanding data, understanding society: using quantitative narratives to embed evidence, argument and data within the undergraduate curriculum
Repeated reports by the ESRC have pointed to the shortage of social science undergraduate students with even basic quantitative methods skills. This is a situation that is harmful to the on-going viability and vitality o...
Funded by: ESRC
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Why might this be relevant?
The project partially answers the question by providing a cross-disciplinary social sciences curriculum that uses quantitative narratives to teach the principles and concepts of quantitative methods. The authors have the necessary expertise in curriculum innovation for quantitative methods teaching.
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Analysing the Reliability of Quantitative Impact Evaluations (ARGIE)
Quantitative impact evaluations – studies which measure the effects of policy – play a central role in government decision-making. In the UK, government departments conduct and commission hundreds of evaluations each yea...
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Why might this be relevant?
Partially addresses the question by focusing on the reliability of quantitative impact evaluations, but does not directly discuss misuse of statistics or data.
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Understanding Society Through Secondary Data Analysis: Quantitative Methods over the Undergraduate Life Course
In the UK we have several large and complex datasets that reveal a lot of information about the characteristics of the general population. One of the main ESRC investments is a new study called "Understanding Societ...
Funded by: ESRC
Lead research organisation: University of Lincoln
Why might this be relevant?
The project partially answers the question by creating new versions of the 'Understanding Society' dataset that are accessible to undergraduate students and developing new teaching resources for students to work through. The authors have the necessary expertise in quantitative methods and data analysis.