A version of this blog post was published at The Conversation on 29/12/20
“It ain’t what you don’t know that gets you in trouble. It’s what you know for sure that just ain’t so.”
Over the course of the COVID-19 pandemic we have seen a deluge of outright lies, conspiracy theoriesand pseudo-sciencefrom various peddlers of self-interest. As a philosopher, more vexing than these calculated examples of misinformation has been the sloppy reasoning evident in public discourse on the international crisis. Every day, the basic failures in critical thinking that I teach first year philosophy students to avoid are being made by politicians, government officials, commentators and the general public. Although these simple errors in reasoning can be employed to deliberately mislead, it is more frequently the well-intentioned that fall victim to their appeal. The only antidote is a large dose of skepticism and some understanding of where our reasoning frequently goes wrong. Here are just three of the common errors to look out for:
1. We are not in Kansas anymore Toto
In arguing against various public health measures, particularly “lockdowns”, it is not uncommon to hear the drops in cancer diagnosesor the negative impacts of school closures (particularly onlow SES students) cited as the “hidden cost” of COVID. Whilst it is reasonable to ask whether the costs of lockdown outweigh the benefits, the costs that failing to impose a lockdown would impose rarely feature in this calculus. Rather, some kind of “pre-COVID normal” is the assumed baseline of comparison. This is a mistake. The rates of cancer diagnosis or school outcomes in pre-COVID times are irrelevant when thinking about the impact of public health measures in our current circumstance. What is relevant is the expected rates and outcomes given the impact of the COVID-19 infections that would occur without the public health measure in place. In the case of cancer rates, for example, we should anticipate rates of diagnoses to be lower than pre-COVID normal both with, and without, lockdowns in place. People are going to avoid going to the doctor in times of a pandemic even if it is easy to do so to reduce their risk of infection. Similarly, when looking at the negative impact of school closures on low SES students, the appropriate comparison must be with the negative impacts that more widespread COVID-19 infections are likely to have on those students. As has been shown both at home and abroad, the impacts of COVID-19 outbreaks are disproportionately felt by lower SES communities and it is reasonable to expect that this will disrupt education in these cohorts too. It is important to look at the costs and benefits of public health interventions but it is important to not to forget that without them, we are looking at the costs of more widespread infection, not “pre-COVID normal”.
2. The Devil is in the detail
In Australia, the overall survival rate for breast cancer in females is 91%. Upon hearing this, it is natural for those newly diagnosed with breast cancer to infer that this means that they have a 91% chance of still being around in 2025. Unfortunately, this is not really the case. For any given individual the details really matter. If the cancer has spread beyond the breast, for example, the survival rate drops to 80%. Factors like age, type of breast cancer and other factors also can make a marked difference. For any individual cancer sufferer the 91% figure is not really all that informative at all. The details here really matter. The same thing is true for the data surrounding COVID-19. Bald numbers hide significant details. NSW and Victoria are illustrative here. In comparing the NSW and Victorian public health responses to the “second wave” the conclusion that the more promising numbers in Sydney as compared to Melbourne must be due solely to differences in contact tracing approaches has been drawn by many, including the PM. This claim relies, however, on an overly simplistic view of the data, ignoring other important differences between the Victorian and NSW situation. Even if we assume equal contact tracing capacity between the two states, differences between the two states offer some reason for one to have had more success in controlling numbers than the other. For example, despite similar absolute case numbers over the ten days to October 14th, approximately 60% of the cases in NSW were due returned international travelers and it accounted for none of the cases in Victoria.Given that a positive case in a traveler in hotel quarantine is easier to contain than one in the general populace, it is natural the situation in Victoria to be more challenging than that in NSW. Similarly there are other features of the demographics of the Victorian outbreak that also set it apart from NSWsuch as the average size of the households in which infected individuals live and the source of their infections. Again, the bald numbers can mislead, the devil is in the detail.
3. Bad luck and chance
The Ancient Greeks blamed unexpected bad outcomes in their lives on Tykhe, the Goddess of chance. The Romans would similarly blame Fortuna. In our controlled modern world, however, we typically assume a bad outcome to be the sign of failure rather than bad luck or chance. In a pandemic such as this one, however, not only can relatively small differences between situations lead to large differences in outcomes, but these small differences often come down to luck. This is especially true when talking about very small numbers of cases such as we have in Australia right now. At such low numbers, bad luck and chance are going play a big role in our fortunesand it helps to keep this in mind. It is easy, for example, to think that any jump in case numbers is indicative of a failure of the public health measures in place. This is a mistake. Whether an COVID-19 positive individual lives with one other person or six, or whether they work in aged care, or from home, can make a significant difference to the potential number of others that they infect with the virus and the potential outcomes of those infections. It is much harder, for example, to contact trace all the people someone working outside of the home has come into contact with compared to someone working from home and only leaving to go to the shops once a week. Thus, no two infections are really equal. This doesn’t mean that jumps in numbers should not be concerning but it also doesn’t mean that it warrants any shift from our current public health measures. Fluctuations are to be expected due to the role that chance and luck unavoidably play in a pandemic situation.