Statistics semantics and Statisticians React to the News

Given I have not found the time to finish one of the many, many draft posts I have started, I am cross-posting most of one I wrote for the Statisticians React to the News blog as the volunteer editor.

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Ten weeks, ten posts.  It seems like a good time for blog editor reflection.

Here in the state of Montana, in the northern Rocky Mountains of the U.S., the air feels different as fall settles in.  We are lucky to be looking forward to weather that will put out local fires and settle smoke drifting in from the west.  But, as far as news, things don’t seem to be changing – COVID-19, politics, and extreme weather events.  And, like the stability of news topics, statisticians’ reactions to the news share many timeless messages.

Statisticians React to the News isn’t meant to be a COVID-19 blog, but it is not surprising it has started with a lot of COVID-19 commentary – that’s what’s in the news, and for good reason.  This blog has been a unique place to capture reactions from authors scattered across the globe (Brazil, Sweden, Italy, USA, Palestine, Philippines).  It has been a weekly reminder of the challenges we all share. We can always use such reminders – making it my favorite part of being involved with this blog.

Contributors have highlighted shared international challenges, such as collecting and reporting quality data in a pandemic; implementing broader testing to include asymptomatic people; understanding sometimes alarming false negative and false positive rates for tests; learning to recognize cognitive biases; the importance of sampling design; and the importance of data to support basic human rights.

We all look forward to a time when COVID-19 isn’t front and center in the news, but for now there is a lot to learn from statisticians’ reactions to the related news. The pandemic has created a common and pressing context highlighting long standing issues with collecting, sharing, and interpreting data.

Challenges discussed on the blog are now personal for many (most?) of us, as the virus continues to spread through our communities.  As I experienced in the last few weeks, a false sense of control and complacency is easy to come by – even for someone who thinks they fully recognize the enormous uncertainties involved in the situation.  Both of my careful, mask-wearing, mid-70’s parents tested positive and for some currently inexplicable reason (I feel very lucky!) made it through with mild symptoms despite my dad’s high risk. In the meantime, I received a negative test result, which was met with “So glad you don’t have COVID!” from most people I told.   I then felt professionally obligated to annoy them with a reminder of false negative rates – information it appeared most would have preferred I kept to myself. Ignorance certainly can be bliss (or at least more comfortable).

As any project, this blog is a work in progress – an adolescent trying new things, searching for its place in the world (I have two teenagers at home and couldn’t resist the analogy).  I am keenly aware of how much content there is to read and/or listen to each day, for better or worse.  I hope this blog will make the cut of earning your precious time and if there’s anything we can do help make that the case, please feel free to let me know.

Finally – I leave you with one reaction of my own.  Statistics semantics.  I have never had a love for things like grammar, spelling, and sentence structure.  But when it comes to Statistics semantics, I just can’t stop thinking about it (plus, the alliteration makes it hard to resist).  Statistics semantics.   In English, we have the phrase “That’s just semantics” – meant to express that you’re trivially worrying about choice of words that don’t affect the meaning (ironically, this phrase makes little sense in the context of Semantics!).

In Statistics, there are so many words that carry different meanings, or even just emphasis, in everyday language as compared to a formal statistical context.  I have grown used to the subtle eye-rolls and half-snarky comments that often come in response to my statistical-wording-pickiness, but to me it is not “just semantics.”   We (in a very royal sense) need to be open to seeing the unintended implications of our word choices (e.g., significant, determine, best, random, answer, calculate, confidence, etc.).  The choices and context do matter – they send implicit messages to the reader. I used to say and write things that I now cringe at, and I’m well aware I will someday cringe at words I automatically use today.  When words and phrases feel a harmless part of cultural norms (including scientific culture), it’s hard to gain the perspective needed to see their potential flaws.  The international voices heard in this blog add another layer of complexity that simultaneously challenges and unites statisticians over words.

To me, Statistics is fundamentally about using information (data and assumptions) to support inferences; and communicating those inferences fundamentally relies on the words we choose.  Regardless of the topic, how “statisticians react to the news” often comes down to the words used or omitted – Statistics semantics.  The same words on a page will be internalized differently by different readers (including different statisticians!) An awareness of the positive and negative implications of our words can play a leading role in improving science communication.  One of my big hopes for this blog is for it to encourage us all to reflect more deeply on the words we use – while at the same time getting a unique dose of international news and perspectives.