Abstract
Media reporting and public discourse in the spring of 2020 has been dominated by the discussion of statistics relating to the COVID-19 outbreak and how to intepret them. Reasoning about these numbers has inspired fear as well as hope in communities worldwide. This environment provides a lens, and a rare scale, for data scientists to investigate how complex statistical topics are communicated to, understood by, and acted upon by diverse audiences. In particular, this crisis has put a premium on âdistributional thinking,â a mindset for reasoning about variation that is front and center in the response to the coronavirus as well as broadly relevant to organizations. This kind of thinking is already widespread among data scientists, but the challenge we face is to instill it across our organizations to equip them to tackle complex problems whose response should be informed by data and evidence. Fortunately, ours is not the first domain to encounter this challenge. I suggest learning from the example of modern social justice movements, who have evolved strategies to generate widespread appreciation of issues with distributional considerations, like the disparate impacts of environmental pollution and inequities in policing. I point to movement-building techniques like participatory research and shared leadership for lessons on how to grow the capacity for distributional thinking within companies, NGOs, agencies, and other organizations.
Highlights
The outbreak of the coronavirus SARS-CoV-2 responsible for the disease designated COVID-19 (Chinazzi et al, 2020; C. Wang et al, 2020) has had tragic impacts already around the world, with further devastation threatened in the months to come (Ferguson et al, 2020)
This environment provides a lens, and a rare scale, for data scientists to investigate how complex statistical topics are communicated to, understood by, and acted upon by diverse audiences. This crisis has put a premium on ‘distributional thinking,’ a mindset for reasoning about variation that is front and center in the response to the coronavirus as well as broadly relevant to organizations. This kind of thinking is already widespread among data scientists, but the challenge we face is to instill it across our organizations to equip them to tackle complex problems whose response should be informed by data and evidence
In terms that will be familiar to data scientists in organizations across varied domains, distributional thinking can be defined as the frame of mind for considering the outcome of a process as not just a singular state of being, but rather a pattern of alternatives and their likelihoods
Summary
The outbreak of the coronavirus SARS-CoV-2 responsible for the disease designated COVID-19 (Chinazzi et al, 2020; C. Wang et al, 2020) has had tragic impacts already around the world, with further devastation threatened in the months to come (Ferguson et al, 2020). One noteworthy outcome from this pandemic is that it has prompted many millions of people to think in terms of distributions This way of thinking has been instrumental to the debate about and design of our governmental and civic response to this crisis. For data scientists, this is a rare opportunity to attract attention to and learn from how complex statistical ideas are communicated to, interpreted by, and responded to across diverse sectors.
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