The False Dichotomies Of COVID-19

The False Dichotomies Of COVID-19

Discussions about science have taken on a frantic edge this year. Where once we could have a fun argument about the pros and cons of a headline, safe in the knowledge that at most it would impact how many walnuts people thought they should eat, in 2020 even the most humdrum of conversations have taken on a darker tone.

This is, perhaps, unsurprising given that we’re dealing with a global pandemic. Bad science costs lives when we’re talking about COVID-19.

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Most stock photos for “bad science” are of astronomy, which I think is very rude. Source: Pexels

And one thing that comes with such acrimonious disputes that is both quintessentially human and very silly is a very simple logical flaw: the false dichotomy. Whether it’s in an argument about masks, or a discussion about the economy, the false dichotomies of COVID-19 are both endless and endlessly frustrating to anyone who values nuance.

So what is a false dichotomy? What should you watch out for in coronavirus arguments?

Let me explain.

Yes Or No Or Maybe

False dichotomies are a pretty basic idea when push comes to shove —they are arguments that have many possible solutions, but are presented as an either/or between only two options. For example, you could ask someone “what’s the best fruit in the world — apples or oranges?”. This is a false dichotomy, because there are hundreds of answers other than apples or oranges, but the question is posed as if those are the only two possible choices.

As humans, we like to present things as either/or, but it’s rarely that simple. It is always so much easier to make an argument as if there are no grey areas, as if the nightmarish complexity that surrounds virtually everything we say and do is totally absent from the one issue that you’re discussing right now.

Unfortunately, it’s almost never true.

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Science, like this blackboard, is filled with strange complexity. Source: Pexels

The reality is that most questions have more than two possible answers, especially when it comes to complex science. Take epidemiology, my area of expertise, for example — there are only a handful of places where there’s an easy black-and-white answer to anything. Does smoking cause cancer? Yes. Do seatbelts save lives? Absolutely. Is asbestos a terrifying danger to your lungs? Very much so.

Are policy solutions to these health hazards always beneficial? Well,it depends.

A classic example of this complexity is the laws surrounding bicycle helmets. Bicycle helmets, when worn, prevent people from getting injured and dying. However, making laws forcing people to wear the helmets reduces the number of people who cycle, and since cycling is a form of active transport that’s beneficial to public health, this can mean that bicycle helmet laws are actually bad for public health. point is beautifully made in Tim Minchin’s The Fence, which I highly recommend listening to because it’s brilliant

So what do we do? Do we implement helmet laws or not? It depends — there’s no easy answer and it varies a lot from place to place.

Now, the point here isn’t about bicycle helmets, which is an ongoing debate in many places, but public health in general. It’s rare that you’ll be able to answer a complex question like “are helmet laws good for public health?” with either an easy yes or no.

Which brings us to COVID-19.

Grey Areas

Most of public health is complex. This goes double for COVID-19, which is a headache-inducing morass of countervailing opinions and evidence regardless of the question asked.

And yet, people are trying to present almost every situation as an either/or scenario. There are few discussions about the costs and benefits of government actions to the economy, and lots of people shouting that lockdowns are always bad and the only other option is to open up completely. You rarely see someone engaging in a reasonable critique of mask laws, it has to be either 100% mask laws forever or you’re a COVID-denier. Either 99.9% of people who get coronavirus are totally asymptomatic or we’re all going to die — it’s often hard to see an in-between.

The problem is, none of these things are as simple as people make them out to be. Take lockdowns, for example. If you asked an epidemiologist whether they thought that lockdowns were a good thing, they’d look at you strangely and tell you that the context was everything. Sometimes, lockdowns appear to have made a substantial difference in terms of the eventual outcomes in an area. Sometimes, they may have been a knee-jerk reaction from a government who could’ve responded in another way. Without knowing the place you are referring to, it’s impossible to answer the question in a meaningful way.

And even within places, the question is more complex than a yes or no. Lockdowns may have impacted the economy of many places, but so has the virus itself. When we balance the scales it’s not health versus the economy, it’s a tangle of health and economic consequences of varying levels of pandemic severity. Countries that have experienced out-of-control epidemics, for example, have notoriously not done that well economically.

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Source: OurWorldInData

In other words, it is a false dichotomy to say that you either have no lockdowns or a ruined economy — in fact, some places that never really locked down have done awfully economically, and some places that did lock down have done really well.

It is, simply put, more complicated than a yes/no — there are innumerable shades of grey.

This is true for pretty much any COVID-19 question you can think to ask, which is why one of the main things that you should look out for in terms of disingenuous or nonsensical arguments are these ridiculous absolutes. Masks can be useful without always being the answer, the virus can be a terrifying infection without killing every person who contracts it, and in general these queries can have more than one answer depending on the situation.

We naturally want things to be simple, but they so rarely are. Embrace the nuance, embrace the uncertainty, because neither is going away any time soon.

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