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You are here: Home / Innovation / Learnings from the COVID crisis for statisticians

Learnings from the COVID crisis for statisticians

By Reine Escalona on 2020-03-30 0

Learnings from the COVID crisis for statisticians
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As statisticians, we are one of the most capable professionals to understand and to relay the right information to the general public about the most talked-about situation – the Corona Virus.

In today’s episode, I want to express what’s on my mind and on my heart about this topic. It’s a bit of an unusual episode, as I’m very passionate about the topic. You can agree or disagree with my thinking. Please let me know what you think about it.

I discuss my learnings, my reflection about this, and what it says about us as statisticians. I covering:

  • Hardly any tables out there, but lots of visualizations both interactive and explanatory – still most of our day-to-day jobs are providing tables – check out these related episodes:
    • Tables are not the key deliverables episode
    • Interview with Zak Skrivanek – a figure says more than 1000 tables
    • Wonderful Wednesday episode
    • VIS SIG
  • Get the data from many different sources – not just the one source at hand: e.g. the study you are currently working on but from RWE, literature data, and other studies
  • There are many misunderstandings on the sources of the data and comparisons across different sources (e.g. different countries) and then inferences on the differences between the policy making in the different countries – where are the statisticians in the news explaining the numbers?
    • We need more leadership!
    • Our associations need to step up and become more professional and impactful!
    • We have a responsibility here!
  • We have a responsibility for providing the numbers but also gives the background and advise on how to use the numbers – for example John Hopkins
    • The FAQ is not really helpful.
    • What can be answered here and what not?
    • How reliable is the data?
    • What are the strength and limitations?
    • Any guidance on the use of the data?
  • Do we train people on how to read our data? Or do we just through the tables over the fence for someone else to deal with it?
  • We can communicate in a powerful way using of scenarios. E.g. the Washington Post article is a great example to show conditional probabilities.

Listen to this timely episode and let me know what you think!

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