During the COVID-19 Pandemic, Leon County in my adopted state of Florida mandated the wearing of face coverings in indoor, public spaces. There were numerous exceptions, such as while exercising (at a distance) and for health reasons. Those who violate the ordinance faced an initial $50 fine which increased to $125 and then up to $250. As would be expected, this ordinance was met with some resistance. Some even claimed that the mask mandate was tyranny.

While discussing the tyranny of the mask during COVID-19 has some historical value, there is also the general issue of whether such health focused mandates are tyrannical. After all, it is just a matter of time before the next pandemic and the state might impose mandates intended to keep people safe. Or it might not, depending on who is in charge.  

One challenge is agreeing on a serious definition of “tyranny” beyond “something I don’t like.” Since American political philosophy is based heavily on John Locke, he is my go-to for defining the term.

Locke takes tyranny to be the “exercise of power beyond right.” For him, the right use of power is for the good of the citizens and a leader’s use of power for “his own private separate advantage” is exercising that power “beyond right.” Locke also presents some other key points about tyranny, noting that it occurs when “the governor, however entitled:

 

  • Makes his will and not the law the rule
  • Does not direct his commands and actions to the preservation of the properties of his people.
  • Directs them to the satisfaction of his own ambition, revenge, covetousness, or any other irregular passion.”

 

Did the ordinance, and similar impositions, meet the definition? On the face of it, it did not. After all, the aim of the ordinance seemed to be for the good of the citizens: it was aimed at reducing the chances of infection. It was also aimed at allowing businesses and other public places to remain. That is, it was aimed at the preservation of the properties of the people. There is no evidence that those in office used the ordinance for their “own private separate advantage” or were trying to satisfy some “irregular passion.”

It could be argued that while the objectives of the ordinance were not tyrannical, the ordinance involved exercising power “beyond right.” That is, the ordinance overstepped the legitimate limits of the power of the governing body. Since I am not a lawyer, I will focus on the moral aspect: do authorities have the moral right to impose a mask requirement or similar health measure on the people?

While people tend to answer in terms of their likes and dislikes, I will follow J.S. Mill and use principles I consistently apply in cases of liberty versus safety. As in all such cases, my first area of inquiry is into the effectiveness of the proposed safety measures. After all, if we are giving up liberty to gain nothing, this would be both foolish and wrong.

While there is some debate over the effectiveness of masks, the consensus of experts is that they do help prevent the spread of the virus. There is also the intuitively plausible argument that face coverings reduce the spread of the virus because they reduce the volume and distance of expulsions. They also block some of what is incoming. Medical professionals have long used these masks for these reasons. In future pandemics, we will also need to evaluate the effectiveness of proposed measures in good faith.

But wearing a mask is not without its costs. Aside from the cost of buying or making masks, they are uncomfortable to wear, they interfere with conversations, and it is hard to look good in a mask. While breathing does require a tiny bit more effort, this is generally not a significant factor for most. Those with pre-existing conditions impacting their breathing are more likely to be severely impacted by COVID-19—but they will need to rationally weigh the relative risks. Anecdotally, I did not find the masks problematic for normal wear, but I used to run wearing a face mask during the Maine winters to keep my face from freezing. That said, the “paper” masks were uncomfortable to wear when they were soaked with sweat, but I was almost always able to rely on distancing while running.

Weighing the effectiveness of the masks against the harm, they seem to have had a decisive safety advantage: by enduring some minor discomfort for short periods of time you could reduce your risk of being infected with a potentially lethal disease. You also reduced the risk of infecting others. Again, whatever measures are proposed during the next pandemic will also need to be assessed in this way.

The second issue to address is whether the gain in safety warrants the imposition on liberty. After all, some people did not want to wear masks, and it is an imposition to require this under the threat of punishment. My go to guide on this is the principle of harm presented by J.S. Mill.

Mill contends that “the only purpose for which power can be rightfully exercised over any member of a civilized community, against his will, is to prevent harm to others.” I will rely on Mill’s arguments for his principle but agree it can be criticized in favor of alternative principles.

During the discussion of his principle Mill argues that we (collectively) have no right to infringe on a person’s liberty just because doing so would be good for them or even to prevent them from harming themselves. As long as their actions impact only themselves, their liberty is absolute. Applying this to the masks, if they only served to protect a person from infection, then Mill’s principle would forbid their imposition: people have the liberty of self-harm. If this had been true, I would have agreed with those who saw masks as tyranny: they have the moral right to put themselves at risk if doing so does not harm others. As they say, their body, their choice.

To use an analogy, If I want to go shooting without wearing any eye protection (and I have medical insurance), I have the right to be stupid and risk losing an eye. But the masks do more than protect the wearer; they also protect other people. If I go out without a mask and I am unaware I am infected, I am putting other people in greater danger—I am potentially harming them. As such, it is no longer just my business, it is their business as well.

Going back to the gun analogy, I do not have a right to just shoot my gun around whenever and wherever I want since doing so puts other people at risk of injury and death. I can be rightfully prevented from doing this. To use another analogy, while I think a person has the moral right to turn off their airbag in their car and face a greater risk of injury or death, they do not have the right to remove their brakes since that would put everyone in danger.

The obvious conclusion is that the imposition of masks was not tyranny. In fact, it is an excellent example of how the state should exercise its power: for the protection of the citizens based on the best available evidence. When the next pandemic arrives, the same approach should be taken. Assuming that the government tries to do anything to address it.

In the previous essay I discussed guilt by association. Not surprisingly, there is an equal but opposite temptation: to refuse to acknowledge bad elements in groups one likes. Giving in to this temptation can result in committing a version of the purity fallacy which could be called the Denial of Association.

This version of the fallacy occurs when a negative claim about a group based on certain members is rejected by asserting, without adequate support, that the alleged members are not true members of the group. This fallacy is also known as the No True Scotsman fallacy thanks to the philosopher Anthony Flew. For example, if a 2nd Amendment rights group is accused of being racist, they might say that those displaying racist symbols at their events were not real members. This version of the fallacy has the following form:

 

Premise 1: Negative claim P has been made about group G based on M members of G.

Premise 2: It is claimed, without support, that the members of M are not true members of G.

Conclusion: Claim P is false.

 

This reasoning is fallacious because simply asserting that problematic alleged members are not true members does not prove that the claim is not true about the group. As always, it is important to remember that fallacious reasoning does not entail that the conclusion is false. A group’s defender could commit this fallacy while their conclusion is correct; they would have simply failed to give a good reason to accept their claim.

Like many fallacies, it draws its persuasive power from psychological factors. Someone who has a positive view of the group has a psychological, but not logical, reason to reject the negative claim. Few are willing to believe negative things about groups they like or identify with. In Flew’s original example, a Scotsman refuses to believe a story about the bad behavior of other Scotsmen on the grounds that no true Scotsman would do such things. People can also reject the claim on pragmatic grounds, such as when doing so would provide a political advantage.

The main defense against this fallacy is to consider whether the negative claim is rejected on principled grounds or is rejected without evidence, such as on psychological or pragmatic grounds. One way to try to overcome a psychological bias is to ask what evidence exists to reject the counterexample. If there is no such evidence, then all that would be left are psychological or pragmatic reasons, which have no logical weight.

Sorting out who or what belongs in a group can be a matter of substantial debate. For example, when people displaying racist symbols show up at gun rights events or protests the question arises as to whether the protesters should be regarded, in general, as racist. Some might contend those openly displaying racist symbols should not define the broader group of protesters. Others contend that by tolerating the display of racist symbols the general group shows that it is racist. As another example, those peacefully protesting police violence generally disavow those who engage in violence and vandalism and claim that the violent protesters do not define their group. Others contend that because violence and looting sometimes occurs adjacent to or after peaceful protests, the protesters are violent looters. College students peacefully protesting Israel’s actions contend that they are not antisemitic and disavow antisemitism, but their right-wing critics claim they are antisemitic. In some cases, there are actual antisemites involved. In other cases, merely criticizing Israel is cast as antisemitic.

Debates over group membership need not be fallacious. If a principled argument is given to support the exclusion, then this fallacy is not committed. For example, if a fictional 2nd amendment rights organization “Anti-Racists for Gun Rights” (ARGR) was accused of being racist because people at their protest displayed racist symbols, showing that none of the racists were members of ARGR would not commit this fallacy.

As another example, if peaceful protesters show that those who engaged in violence and looting are not part of their group, then it would not be fallacious for them to reject the claim that they are violent on the grounds that those committing the violence are not in their group. As a third example, if college students peacefully protesting Israel show that the people shouting antisemitic slogans at the protest were neo-Nazis from off campus, then they would not be committing this fallacy.

Sorting out which people belong to a group and how the group should be defined can be challenging; but should be done in a principled way. To define a group by the worst of those associated with it runs the risk of committing the guilt by association fallacy. Denying that problematic members are not true members of a group runs the risk of committing the denial of association fallacy. While both fallacies are psychologically appealing and can be highly effective means of persuasion, they have no merit as arguments.

As a practical matter, the unprincipled use both fallacies in efforts to advance their goals in bad faith. After all, what matters to them is “winning” rather than what is true and good.

It is tempting to define a group you do not like by the worst people associated with it, but this can lead to committing the fallacy of guilt by association. To illustrate, conservative protests sometimes include people openly displaying racist symbols and this can lead leftists to conclude that all the protestors are racists. As another example, protests against Israel’s actions sometimes include people who make antisemitic statements, and this leads some people to categorize the protests as antisemitic. While this is often done in bad faith, people can sincerely make unwarranted inferences about protests from the worst people present.

Since people generally do not make their reasoning clear, it often must be reconstructed. One possible line of bad reasoning is the use of a hasty generalization. A hasty generalization occurs when a person draws a conclusion about a population based on a sample that is not large enough to adequately support the concussion. It has the following form:

 

Premise 1: Sample S (which is too small) is taken from population P.

Premise 2: In Sample S X% of the observed A’s are B’s.

Conclusion: X% of all A’s are B’s in Population P.

 

This is a fallacy because the sample is too small to warrant the inference. In the case of the protesters, inferring that most conservative protesters are racists based on some of them displaying racist symbols would be an error. Likewise, inferring that most people protesting Israel are antisemitic because some of them say antisemitic things would also be an error. At this point it is likely that someone is thinking that even if most conservative protesters are not open racists, they associate with them—thus warranting the inference that they are also guilty. Likewise, someone is probably thinking that people protesting Israel are guilty of antisemitism because of their association with antisemites. This leads us to the guilt by association fallacy.

The guilt by association fallacy has many variations but this version occurs when it is inferred that a group or individual has bad qualities because of their  (alleged) association with groups or individuals who have those qualities. The form of the fallacy is this:

 

Premise 1: Group or person A is associated with group or person B

Premise 2: Group or person B has (bad) qualities P, Q, R.

Conclusion: Group A has (bad) qualities P, Q, R.

 

The error is that the only evidence offered is the (alleged) association between the two. What is wanting is an adequate connection that justifies the inference. In the conservative protester example, the protesters might be associated with protesters displaying racist symbols, but this is not enough to warrant the conclusion that they are racists. More is needed than a mere association. The more is, as one would imagine, a matter of considerable debate: those who loath conservatives will tend to accept relatively weak evidence as supporting their biased view; those who like the protesters might be blind even to the strongest evidence. Likewise for people protesting Israel. But whatever standards are used to judge association, they must be applied consistently—whether one loathes or loves the group or person.

As noted above, people who have protested Israel have been accused of association with antisemites. But the same standards applied to conservative protesters need to be applied: to infer that because some protesters have been observed to be antisemitic then most (or all) are as well would commit the hasty generalization fallacy. Naturally, if there is evidence showing that most conservative protesters are racist or evidence showing that most (or all) people who protest Israel are antisemitic, then the fallacy would not be committed.

To infer that those protesting Israel are antisemitic because some associated with the protests are antisemitic would commit the guilt by association fallacy, just as the fallacy would be committed if one inferred that conservative protesters are racists because they are associated with racists. Obviously, if there is adequate evidence supporting these claims, then the fallacy would not be committed.

While most Americans initially supported the lockdown, a fraction of the population  engaged in (often armed) protests. While the topic of protests is primarily a matter for political philosophy and ethics, critical thinking applies here as well. Given the political success of the anti-health movement in America, we can expect protests against efforts to mitigate the next pandemic. Assuming that any efforts are made.

While the protests were miniscule in size relative to the population of the country, they attracted media attention—they made the national news regularly and the story was repeated and amplified. On the one hand, this makes sense: armed protests against efforts to protect Americans from the virus was news. On the other hand, media coverage is  disproportional to the size and importance of the protests.  The “mainstream” media is often attacked as having a liberal bias and while that can be debated, it the media does have a bias for stories that attract attention. Public and private news services need stories that draw an audience. Protests, especially by people who are armed, draw an audience.  It can also be argued that some news services have a political agenda that was served by covering such stories.

While it can be argued that such stories are worth covering in the news, disproportional coverage can lead people to commit the Spotlight Fallacy. This fallacy is committed when a person uncritically assumes that the degree of media coverage given to something is proportional to how often it occurs or its importance. It is also committed when it is uncritically assumed that the media coverage of a group is representative of the size or importance of the group.

 

Form 1

Premise 1: X receives extensive coverage in the media.

Conclusion: X occurs in a frequency or is important proportional to its coverage.

 

Form 2

Premise 1: People of type P or Group G receive extensive coverage in the media.

Conclusion: The coverage of P or G is proportional to how P and G represent the general population.

 

This line of reasoning is fallacious since the fact that someone or something attracts the most attention or coverage in the media does not mean that it is representative or that it is frequent or important.

It is like the fallacies Hasty Generalization, Biased Sample and Misleading Vividness because the error being made involves generalizing about a population based on an inadequate or flawed sample.

In the case of the lockdown protests, the protests were limited in occurrence and size, but the extent of media coverage conveyed the opposite. The defense against the Spotlight Fallacy is to look at the relevant statistics. As noted above, while the lockdown protests got a great deal of coverage, they were small events that were not widespread. This is not to say that they have no importance. As such we should look at such protests not through the magnifying glass of the media but through the corrective lenses of statistics. I now turn to an ad hominem attack on the protesters.

Some critics of the protesters pointed out that the protesters were also being manipulated by an astroturfing campaign. Astroturfing is a technique in which the true sponsors of a message or organization create the appearance that the message or organization is the result of grassroots activism. In the case of the lockdown protests, support and organization was being provided by individuals and groups supporting Trump’s re-election and who were more concerned with a return to making money than the safety of the American people.  While such astroturfing is a matter of concern, to reject the claims of the protesters because they are “protesting on AstroTurf” rather than standing on true grassroots would be to commit either an ad hominem or genetic fallacy.

An ad hominem fallacy occurs when a person’s claim is rejected because of some alleged irrelevant defect about the person. In very general terms, the fallacy has this form:

 

Premise 1: Person A makes claim C.

Premise 2: An irrelevant attack is made on A.

Conclusion: C is false.

 

This is a fallacy because attacking a person does not disprove the claim they have made. In the case of a lockdown protester, rejecting their claims because they might be manipulated by astroturfing would be a fallacy. As would rejecting their claims because of something one does not like about them.

If the claims made by the protesters as a group were rejected because of the astroturfing (or other irrelevant reasons) then the genetic fallacy would have been committed. A Genetic Fallacy is bad “reasoning” in which a perceived defect in the origin of a claim or thing is taken to be evidence that discredits the claim or thing itself. Whereas the ad hominem fallacy is literally against the person, the genetic fallacy applies to groups. The group form looks like this:

 

Premise 1: Group A makes claim C.

Premise 2: Group A has some alleged defect.

Conclusion: C is false.

 

While it is important to avoid committing fallacies against the protesters, it is also important to avoid committing fallacies in their favor. Both the ad hominem and genetic fallacy can obviously be committed against those who are critical of the protesters. For example, if someone dismisses the claim that the protesters are putting themselves and others at needless risk by asserting that the critic “hates Trump and freedom”, then they would be committing an ad hominem. The same will apply to future protests about responses to pandemics. Again, assuming there will be a response.

To many Americans the protests seemed not only odd, but dangerously crazy. This leads to the obvious question of why they occurred. While some might be tempted to insult and attack the protesters under the guise of analysis, I will focus on a neutral explanation that is relevant to critical thinking. This analysis should also be useful for thinking about the next pandemic.

One obvious reason for the protests is that the lockdown came with an extremely high price—people had good reason to dislike it and this could have motivated them to protest. But there is more to it than that. The protests were more than people expressing their concerns and worries about the lockdown. They were political statements and thoroughly entangled with other factors that included supporting Trump, anti-vaccination views, anti-abortion views, second-amendment rights and even some white nationalism. This is not to claim that every protester endorsed all the views expressed at the protests. Attending a protest about one thing does not entail that a person supports whatever is said by other protesters. Because people try to exploit protests for their own purpose it is important to distinguish the views held by various protestors to avoid falling into assigning guilt by association. That said, the protests were an expression of a polarized political view and it struck many as odd that people would be protesting basic pandemic precautions.

One driving force behind this was what I have been calling the Two Sides Problem. While there are many manifestations of this problem, the idea is that when there are two polarized sides, this provides fuel and accelerant to rhetoric and fallacies—thus making them more likely to occur. Another aspect of having two sides is that it is much easier to exploit and manipulate people by appealing to their membership in one group and their opposition to another.

In the case of the protests, there was a weaponization of public health. Those who recommend the lockdown are expert and there is a anti-expert bias in the United States. The weaponization of the crisis to help the political right followed the usual tactics: disinformation about the crisis, claims of hoaxes, scapegoating, anti-expert rhetoric, conspiracy theories and such. Part of what drove this was in-group bias: the cognitive bias that inclines people to assign positive qualities to their own group while assigning negative qualities to others. This also applies to accepting or rejecting claims.

This weaponization was not new or unique to the COVID-19 pandemic. American politics has been marked by politicizing and weaponizing so that one side can claim a short-term advantage at the cost of long-term harm. Critical thinking requires us to be aware of this and to be honest about the cost of allowing this to be a standard tool of politics.

While there were many aspects to the lockdown protests, one of the core justifications was that the lockdown was a violation of Constitutional rights. The constitutional aspect is a matter of law, and I leave that to experts in law to debate. There is also the ethical aspect—whether the lockdown is morally acceptable, and this issue can be cast in terms of moral rights.  This discussion would take us far afield into the realm of moral philosophy, but I will close with an analogy that might be worth considering.

While the protesters were against the lockdown in general, opposition to wearing masks was the focus of the complaints. While there was rational debate about the efficacy of masks, the moral argument advanced was that the state does not have the right to compel people to wear masks. It can also be presented in terms of people having rights that the state must respect. One possibility is that people have the right to decide what parts of the body they wish to cover. If so, the obvious analogical argument is that if this right entitles people to go without masks, it also entitles people to go without any clothes they choose not to wear. If imposing masks is an act of oppression, then so is imposing clothing in general.

Another possible right is the right to endanger others or at least freely expose other people to bodily “ejections” they do not wish to encounter. If there is such a right, then it could be argued that people have a right to fire their guns and drive as they wish, even if doing so is likely to harm or kill others. If there is a right to expose other people to physical bodily ejections that they do not want to be exposed to, then this would entail that people have the right to spit and urinate on other people. This all seems absurd.

As a practical matter, people are incredibly inconsistent when it comes to rights and restrictions, so I would expect some people to simply dismiss these analogies because they did not want to wear masks but probably do not want people running around naked. But if masks were an act of oppression, so are clothes.

When the next pandemic arrives, we can expect similar protests against efforts to combat it. But this assumes that efforts will be taken, which will depend on who is running America during the next pandemic.

One stock argument against social distancing and other restricted responses to the COVID-19  pandemic was to conclude  these measures should not have been taken because we do not take similar approaches to comparable causes of death. In the next pandemic, we can expect the same reasoning which can be formalized as follows:

 

Premise 1: Another cause of death kills as many or more people.

Premise 2: We do not impose X measures to address this cause of death.

Conclusion: We should not impose X measures to address the pandemic.

 

Those making the argument often used the flu as an analogous cause of death, but there were also comparisons to automobile accidents, suicides, heart disease, drowning in pools and so on. While the specific arguments were presented in various ways, they were all arguments from analogy. Or at least attempts.

Informally, an argument by analogy is an argument in which it is concluded that because two things are alike in certain ways, they are alike in some other way. More formally, the argument looks like this:

 

            Premise 1: X and Y have properties P, Q, R.

            Premise 2: X has property Z.

            Conclusion: Y has property Z.

 

X and Y are variables that stand for whatever is being compared, such as causes of death. P, Q, R, and are also variables, but they stand for properties or features that X and Y are known to possess, such as killing people. Z is also a variable, and it stands for the property or feature that X is known to possess, such as not being addressed with social distancing. The use of P, Q, and R is just for the sake of the illustration—the things being compared might have many more properties in common.

An argument by analogy is an inductive argument. This means that it is supposed to be such that if all the premises are true, then the conclusion is probably true. Like other inductive arguments, the argument by analogy is assessed by applying standards to determine the quality of the logic. Like all arguments, there is also the question of whether the premises are true. The strength of an analogical argument depends on three factors. To the extent that an analogical argument meets these standards it is a strong argument.

First, the more properties X and Y have in common, the better the argument. This standard is based on the commonsense notion that the more two things are alike in other ways, the more likely it is that they will be alike in some other way. It should be noted that even if the two things are alike in many respects, they might not be alike in terms of property Z. This is one reason why analogical arguments are inductive.

Second, the more relevant the shared properties are to property Z, the stronger the argument. A specific property, for example P, is relevant to property Z if the presence or absence of P affects the likelihood that Z will be present. It should be kept in mind that it is possible for X and Y to share relevant properties while Y does not actually have property Z. Again, this is part of the reason why analogical arguments are inductive.

Third, it must be determined whether X and Y have relevant dissimilarities. The more dissimilarities and the more relevant they are, the weaker the argument.

These can be simplified to a basic standard: the more like the two things are in relevant ways, the stronger the argument. And the more the two things are different in relevant ways, the weaker the argument. So, using these standards let us consider the cause of death analogy.

One thing that all causes of death do have in common is that they are causes of death. This is true of everything from swimming pools to the flu to COVID-19. Obviously, different causes of death will be more or less like COVID-19 or other pandemic caused deaths and a full consideration would require grinding through each argument to see if it holds up. In the interest of time, I will consider two main categories of causes of death that should encompass most (if not all) causes.

One category of causes of death consists of those that cannot be addressed by social distancing and the other science-based approaches to COVID-19 and other likely pandemic pathogens. These include such things as suicide, traffic fatalities and swimming pool deaths.  We obviously do not use social distancing to address these causes of death because they would not work. As such, arguing that because we do not use social distancing to combat traffic deaths so we should not use it to combat COVID-19 (or another pathogen) would be a terrible analogy. To use an analogy, this would be like arguing that since we do not use air bags and seat belts to address pandemics, we should not use them to reduce traffic fatalities. This would be bad reasoning. 

To be fair, someone could argue that what matters is not the specific responses but the degree of the response. That is, since we do not have a massive and restrictive response to traffic fatalities, we should not have had a massive response to COVID-19 or have a similar response to the next pandemic. While it is rational to make a response proportional to the threat, the obvious reply to this argument is that we do have a massive and restrictive response to traffic fatalities. Vehicles must meet safety standards, drivers must be licensed, there are books of traffic laws, traffic is strictly regulated with signs, lights and road markings, and the police patrol the roads regularly. Even swimming pools are heavily regulated in the United States. For example, fences and self-locking gates are mandatory in most places. Somewhat ironically, drawing an analogy to things like traffic fatalities supports massive and restrictive means of addressing a pandemic.

Horribly, the best (worst) way to argue against a strong response to a pandemic would be to find a cause of death on the scale of the pandemic that we as a nation do little about and then argue that the same neglect should be applied to the pandemic. Poverty and lack of health care are two examples.  This analogy would certainly appeal to evil people.

The second category of deaths consists of causes that could be addressed using the same methods used to address a pandemic. The common flu serves as an excellent example here. The same methods that work against COVID-19 and many other pathogens also work against the flu. As many argued, even in a bad flu year life remains normal: no social distancing, no closing of businesses, no mandatory masks. While this analogy seems appealing, it falls apart quickly because of the relevant differences between COVID-19 and the common flu. The same would also apply to the next dangerous pandemic.

When people started advancing death analogies against pandemic responses, the best available estimate about COVID-19 was that it killed 3-4% of those infected—though there was considerable variation based on such factors as age, access to health care, and underlying health conditions.  In contrast, the flu has a mortality rate well below .1%. It kills too many people but was less dangerous than COVID-19. So, it makes sense to have a more restrictive and extensive response to something that is more dangerous. We also have(or had) measures in place against the flu: people are urged to take precautions and flu shots are recommended. As this is being written, people feel that the threat of COVID is like that of the seasonal flu and acting accordingly. Likewise, if a dangerous strain of flu emerges again, then it would make sense to step up our restrictions.

In closing, this discussion does lead to a matter of ethics and public policy. As those who make the death analogies note, we collectively tolerate a certain number of preventable deaths. We must seriously address the issue of determining the acceptable number of deaths from the next pandemic and match our response to that judgment. And we should not forget that we might be among those tolerated deaths. We should also consider why we do tolerate so many other deaths.

Stay safe and I will see you in the future.

During the COVID-19 pandemic some public figures and social media users attempted to downplay the danger of COVID-19 by comparing the number of deaths caused by the virus to other causes of deaths.  For example, a common example noted that 21,297 people died from 1/2/202 to 3/25/200 from COVID-19 but that 113,000 people died from the flu during the same period.

Downplaying is a rhetorical technique used to make something seem less important or serious. These comparisons seemed aimed at dismissing claims made by experts that the virus was a serious threat. The comparisons were also often used to persuade people that the response was excessive and unnecessary. While comparing causes of death is useful when judging how to use resources and accurately assess threats, the death comparisons must be done with a critical eye. That was true in the last pandemic, and it will be true in the next one.

Before even considering the comparison between pandemic deaths and other causes of death, it is important to determine the accuracy of the numbers. If the numbers are exaggerated, downplayed or otherwise inaccurate, then this undermines the comparison. Even if the numbers are accurate, the comparison must be critically assessed. The methods I will discuss are those I use in my Critical Inquiry class and are drawn from Moore and Parker’s Critical Thinking text. When an important comparison is made, you should ask four questions:

 

  1. Is important information missing?
  2. Is the same standard of comparison being used? Are the same reporting and recording practices being used?
  3. Are the items comparable?
  4. Is the comparison expressed as an average?

 

While question 4 does not apply, the other three do. One important piece of missing information in such comparisons is that while the other causes of death tend to be stable over time, the deaths caused by COVID-19 grew exponentially. On March 1 the WHO reported 53 deaths that day. 862 deaths were reported on March 16. On March 30 there were 3215 new deaths. On April 8 the United States alone  had 1,997 deaths and 14.390 people are believed to have died in the United States since the start of the pandemic. The death toll kept rising. In contrast, while seasonal flu deaths fluctuate, they do not grow in this exponential manner. As such, the comparison is flawed. We can expect similar comparisons to be made in the next pandemic and should be on guard against erroneous comparisons of this sort.

Another flaw in the comparison is that the flu and many other causes of death are well established. The COVID-19 virus was still spreading when the comparison was made. It would be like comparing a fire that just started with a fire that has been steadily burning and confidently claiming that the new fire would not be as bad as the old fire.

Death numbers are also most likely an estimate from past yearly death tolls. What the numbers reflect is the number of people who probably died of those causes during a few months based on data from previous years. 

While the death toll from COVID-19 was high, COVID-19 deaths were also likely to be underreported. Since testing was limited for quite some time, some people who died from the virus did not have their cause of death properly reported. Even in the early days of the death comparison, the deaths caused by COVID-19 were most likely higher than reported. This leads to two problems with the comparison. One is that if the other causes of death are accurately reported and COVID-19 deaths were not, then the comparison is flawed. The second is that COVID-19 deaths might have been recorded as being caused by something else (such as the flu/pneumonia) and this would also make the comparison less accurate by “increasing” the number of deaths by other causes. 

While the comparison to other causes of death might have seemed persuasive early in the pandemic, the exponential increase in deaths is like to have robbed the comparison of its persuasive power. In mid-April, COVID-19 was killing more Americans per week than automobile accidents, cancer, heart disease and the flu/pneumonia did in 2018.  Somewhat ironically, a comparison of COVID-19 deaths ended up showing the reverse of what the comparison was originally intended to do.

We can expect similar death comparisons in the early days of the next pandemic. While these comparisons can have merit, they are often used as rhetorical devices to downplay the seriousness of a pandemic. As such, we should be on guard against this tactic during the next pandemic.

In the last essay I looked at the inductive generalization and its usefulness in reasoning about pandemics and ended by mentioning that there are various fallacies that can occur when generalizing. The most common are hasty generalization, appeal to anecdotal evidence, and biased generalization. I will look at each of them in terms of pandemics.

A hasty generalization occurs when a person draws a conclusion about a population based on a sample that is not large enough to adequately support the concussion. It has the following form:

 

Premise 1: Sample S (which is too small) is taken from population P.

Premise 2: In Sample S X% of the observed A’s are B’s.

Conclusion: X% of all A’s are B’s in Population P.

 

In the previous essay I presented a rough guide to sample size,  margin of error and confidence level. In that context, this fallacy occurs when the sample is not large enough to warrant confidence in the conclusion. In the case of a pandemic, one important generalization involves sorting out the lethality of the pathogen. Math for this is easy but the challenge is getting the right information.

During the COVID-19 pandemic, there were large samples of infected people.  As such, inferences from these large samples to the lethality of the virus would not be a hasty generalization. But avoiding this fallacy does not mean that the generalization is a good one as there are other things that can go wrong.

There were also inferences drawn from relatively small samples, such as generalizations from treatments undergoing testing. For example, the initial samples of people treated with hydroxychloroquine for COVID-19 were small, so generalizing from those samples risked committing a hasty generalization. This isn’t to say that small samples are always useless but due care must be taken when generalizing from them.

As a practical guide, when you hear claims about a pandemic (or anything) based on generalizations you need to consider whether the conclusion is supported by an adequately sized sample. While having a sample that is too small does not entail that the conclusion is false (that inference would be fallacious) but the conclusion would not be adequately supported. While a small sample provides weak logical support, an anecdote can have considerable psychological force which leads to a fallacy similar to the hasty generalization.

An appeal to anecdotal evidence is committed when a person draws a conclusion about a population based on an anecdote (a story) about one or very few cases. The fallacy is also committed when someone rejects reasonable statistical data supporting a claim in favor of a single example or small number of examples that go against the claim. There are two forms for this fallacy:

 

Form One

Premise 1:  Anecdote A is told about a member M (or small number of members) of Population P.

Premise 2: Anecdote A says that M is (or is not) C.

Conclusion: Therefore, C is (or is not) true of Population P.

 

Form Two

Premise 1:  Good statistical evidence exists for general claim C.

Premise 2: Anecdote A is is an exception to or goes against general claim C.

Conclusion: C is false.

 

This fallacy is like hasty generalization in that an inference is drawn from a sample too small to adequately support the conclusion. One difference between hasty generalization and anecdotal evidence is that the fallacy of anecdotal evidence involves using a story (anecdote) as the sample. Out in the wild it can be difficult to distinguish as hasty generalization from anecdotal evidence. Fortunately, what is most important is recognizing that a fallacy is occurring. A much clearer difference is that the paradigm form of anecdotal evidence involves rejecting statistical data in favor of the anecdote.

People often fall victim to this fallacy because anecdotes usually have much more psychological force than statistical data. Wanting an anecdote to be true also fuels this fallacy. During the COVID-19 pandemic, there were many anecdotes about alleged means of curing or preventing or curing the disease and the same will happen during the next pandemic. Even if the anecdotes are not lies, they do not provide an adequate basis for drawing conclusions about the general population. This is because the sample is not large enough to warrant the conclusion. As a concrete example, while there were some early positive anecdotes about hydroxychloroquine. Then wishful thinking and Trump’s claims caused some people to accept the anecdotes as adequate evidence, but this was bad reasoning.

Appeals to anecdotal evidence often occur in the context of causal reasoning, such as the case of hydroxychloroquine, and this adds additional complexities.

As with any fallacy, it does not follow that the conclusion of an appeal to anecdotal evidence is false. The error is accepting the conclusion based on inadequate evidence, not in making a false claim. It is also worth noting that anecdotal evidence can be useful for possible additional investigation but is not enough to prove a general claim.

As noted earlier, there were large samples of infected people that allowed generalizations to be drawn without committing the fallacy of hasty generalization. But even large samples can be problematic. This is because samples need to be both large enough and representative enough. This takes us to the fallacy of biased generalization.

This fallacy is committed when a person draws a conclusion about a population based on a sample that is biased to a degree or in a way that prevents it from adequately supporting the conclusion.

 

Premise 1: Sample S (which is too biased) is taken from population P.

Premise 2: In Sample S X% of the observed A’s are B’s.

Conclusion: X% of all A’s are B’s in Population P.

 

The problem with a biased sample is that it does not represent the population adequately and so does not adequately support the conclusion. This is because a biased sample can differ in relevant ways from the population that affects the percentages of A’s that are B’s.

In the case of COVID-19 there was a serious problem with biased samples, although the situation improved over time. I will focus on an inductive generalization about the lethality of the virus.

The math for calculating lethality is easy but the main challenge is sorting out how many people are infected.   Since the start of the pandemic, the United States had a self-inflicted shortage of test kits. Because of this, many of the available tests were being used on people showing symptoms or who were exposed to those known to be infected. This sample was sample was large but biased: it contained a disproportionate number of people who were already showing symptoms and missed many people who were infected but asymptomatic.

If we face a similar situation in the next pandemic, the sample will probably have a lethality rate higher than the real lethality rate. To use a simple fictional example: imagine a population of 1000 people and 200 of them are infected with a virus. Of the 200 people infected, 20 show symptoms and only they are tested.  Of the 20 people tested, 2 die. This sample would show a mortality rate of 10%. But the actual mortality rate would be 2 in 200 which is 1 %.  This would still be bad, but not as bad as the biased sample would indicate. This shows one of the many reasons why broad testing is important: it is critical to establish an accurate lethality rate. An accurate lethality rate is essential to making rational decisions about our response to any pandemic.

As a final point, it is also important to remember that the lethality varies between groups in the overall population—we know this based on the death data. But to determine the lethality for each group, the samples used for the calculation must be representative of the population. While overall lethality is important, making rational decision making also requires knowing the lethality for various groups. For example, pathogens tend to be more lethal for seniors and they would need more protection in the next pandemic.

As always, stay safe and I will see you in the future.

During a pandemic, like that of COVID-19, it might be wondered how the number of cases is determined and how the lethality of a disease is determined. Some might be concerned or skeptical because the numbers often change over time and they usually vary across countries, age groups, ethnicities and economic classes. This essay provides a basic overview of a core method of making inferences from samples to entire populations, what philosophers call the inductive generalization.

An inductive generalization is an inductive argument. In philosophy, an argument consists of premises and one conclusion. The premises are the reasons or evidence being offered to support the conclusion, which is the claim being argued for.  Philosophers often divide arguments into inductive and deductive. In philosophy a deductive argument is such that the premises provide (or are supposed to provide) complete support for the conclusion. An inductive argument is an argument such that the premises provide (or are supposed to provide) some degree of support (but less than complete support) for the conclusion.  If the premises of an inductive argument support the conclusion adequately (or better) it is a strong argument. It is such that if the premises are true, the conclusion is likely to be true. If a strong inductive argument has all true premises, it is sometimes referred to as being cogent.

One feature of inductive logic is that a strong inductive argument can have a false conclusion even when all the premises are true. This is because of what is known as the inductive leap: the conclusion always goes beyond the premises. This can also be put in terms of drawing a conclusion from what has been observed to what has not been observed. The now dead David Hume argued back in the 1700s that this meant we could never be sure about inductive reasoning and later philosophers called this the problem of induction. In practical terms, this means that even if we use perfect inductive reasoning using premises that are certain, our conclusion can still be false. But induction is often the only option, and we use it because we must. So, when the initial numbers about COVID-19 turned out to be wrong, this is exactly what we should expect. The same must be expected in the next pandemic.

What, then, is an inductive generalization? Roughly put, it is an argument in which a conclusion about an entire population is based on evidence from a sample of observed members of that population. The formal version looks like this:

                                    Premise 1: P% of observed Xs are Ys.

                                    Conclusion: P% of all Xs are Ys.

 

The observed Xs would be the sample and all the Xs would be the target population. As an example, if someone wanted to know the mortality rate during a pandemic for males over sixty, the target population would be all males over sixty.

While the argument is simple, sorting out when a generalization is strong can be challenging. Without getting into the statistics and methods for doing rigorous generalizations, I will go over the basic method of assessment—so you can make some sense when experts talk about such matters during the next pandemic.

There will be various factors whose presence or absence in the sample can affect the presence or absence of the property the argument is concerned with, so a representative sample will have those factors in proportion to the target population. For example, if we wanted to determine the infection rate for all people, then we would need to try to ensure that our sample included all factors affecting the infection rate and our sample would need to mirror our target population in terms of age, ethnicity, base health, and all other relevant features. Sorting out what factors are relevant can be challenging, especially as a pandemic is unfolding. To the degree that the sample mirrors the target population properly, it would be representative.

A sample is biased relative to a factor to the extent that the factor is not present in the sample in the same proportion as in the population. This sort of sample bias was a problem when trying to generalize about COVID-19. One example of this was trying to draw a conclusion about the lethality of COVID-19. While the math to do this is easy (a simple calculation of the percentage of the infected who die from it) getting the numbers right is hard because we needed to know how people were infected and how many died from it.

Experts tried to determine the number of people infected by testing and modeling, which are also inductive reasoning. In the United States, most of the testing was of people showing symptoms, and this created a biased sample—to get an unbiased sample, even those without symptoms should be tested.  There was also the practical matter of the accuracy of the tests and the determination of the cause of death. This will be true in the next pandemic as well.

To use a concrete, but made up example, if 5% of those who tested positive for COVID-19 ended up dying, the generalization from that sample to the whole population would only be as strong as the representativeness of the sample. If only sick people were tested, the sample will not be representative and the conclusion about the lethality of the virus will (probably) be wrong.

There is also the challenge of sorting out the effect of the virus on different populations. While there is an overall infection rate and lethality rate for the whole population, there are different infection rates and lethality rates for different groups within the human population. As an example, the elderly were more likely to die of COVID-19 than younger people.

In addition to representativeness, sample size is important; the larger, the better.  This brings us to two more concepts: Margin of error and confidence level. A margin of error is a range of percentage points within which the conclusion of inductive generalization falls; this number is usually presented in terms of being plus or minus. The margin of error depends on sample size and the confidence level of the argument. The confidence level is typically presented as a number and represents the percentage of arguments like the one in question that have a true conclusion.

When generalizing about large (1o,000+) populations, a sample will need to have 1,000+ individuals to be representative (assuming the sample is taken properly). This table, from Moore & Parker’s Critical Thinking text, shows the connection between sample size and error margin (confidence level of 95%:

 

Sample Size

Error Margin (%)

Corresponding Range (percentage points)
10 +/- 30 60
25 +/- 22 44
50 +/- 14

28

100 +/- 10 20
250 +/- 06 12
500 +/- 04 8
1,000 +/- 03 6
1,500 +/- 02 4

 

The practical takeaway is that sample size is important: a small sample will have a large margin of error that can make it useless. For example, suppose that a group of 50 COVID-19 patients received hydroxychloroquine tablets and 10 of them recovered fully. Laying aside all causal reasoning (which would be a huge mistake) the best we could say is that 20% of patients treated with hydroxychloroquine +/-14% will recover fully. This is just a simple generalization and a controlled experiment or study would be needed to properly assess a causal claim.

There are various fallacies (mistakes in reasoning) that can occur with a generalization. I will discuss those in the next essay. Stay safe and I will see you in the future.

During a White House press briefing President Trump expressed interest in injecting disinfectants as a treatment for COVID-19. In response  medical experts and the manufacturers of Lysol warned the public against attempting this. Trump’s defenders adopted two main strategies. The first was to interpret Trump’s statements in a favorable way; the second was to assert they were “fact checking” the claim that Trump told people to inject disinfectant. Trump eventually claimed that he was being sarcastic to see what the reporters would do. From the standpoint of critical thinking, there is lot going on with rhetorical devices and fallacies. I will discuss how critical thinking can sort through this sort of situation, because the next pandemic is likely to see a repeat performance.

When interpreting or reconstructing claims and arguments, philosophers are supposed to apply the principle of charity. Following this principle requires interpreting claims in the best possible light and reconstructing arguments to make them as strong as possible. There are three reasons to follow the principle. The first is that doing so is ethical. The second is that doing so avoids committing the straw person fallacy, which I will talk more about in a bit. The third is that if I am going to criticize a person’s claims or arguments, criticism of the best and strongest versions also takes care of the lesser versions.

The principle of charity must be tempered by the principle of plausibility: claims must be interpreted, and arguments reconstructed in a way that matches what is known about the source and the context. For example, reading quantum physics into the works of our good dead friend Plato would violate this principle.

Getting back to injecting disinfectants, it is important to accurately present Trump’s statements in context and to avoid making a straw person. The Straw Person fallacy is committed when one ignores a person’s actual claim or argument and substitutes a distorted, exaggerated or misrepresented version of it. This sort of “reasoning” has the following pattern:

 

Premise 1: Person A makes claim or argument X.

Premise 2: Person B presents Y (which is a distorted version of X).

Premise 3: Person B attacks Y.

Conclusion: Therefore, X is false/incorrect/flawed.

 

This sort of “reasoning” is fallacious because attacking a distorted version of a claim or argument is not a criticism of the original. This fallacy often uses hyperbole, a rhetorical device in which one makes an exaggerated claim. A straw serson can be effective because people often do not know the real claim or argument being attacked. The fallacy is especially effective when the straw person matches the audience’s biases or stereotypes as they will feel that the distorted version is the real version.

While this fallacy is usually aimed at an audience, it can be self-inflicted: a person can unwittingly make a Straw Person out of a claim or argument. This can be done entirely in error (perhaps due to ignorance) or due to the influence of prejudices and biases.

The defense against a Straw Man, self-inflicted or not, is to get a person’s claim or argument right and to apply the principle of charity and the principle of plausibility.

Some of Trump’s defenders claimed Trump was the victim of a straw person attack; they set off on a journey of “fact checking” and asserted that Trump did not tell people to drink bleach. Somewhat ironically, they might have engaged in Straw Person attacks when attempting to defend Trump from alleged Straw Person attacks. Warning people to not drink bleach or inject disinfectants is not the same thing as claiming that Trump told people to do these things.

The truth is that Trump did not tell people to drink bleach. His exact words, from the official White House transcript, are: “And then I see the disinfectant, where it knocks it out in a minute. One minute. And is there a way we can do something like that, by injection inside or almost a cleaning. Because you see it gets in the lungs and it does a tremendous number on the lungs. So it would be interesting to check that. So, that, you’re going to have to use medical doctors with. But it sounds — it sounds interesting to me.”

Trump did not tell people to drink bleach or inject disinfectant. As such, the “Clorox Chewables” and similar memes were a form of visual Straw Person attack against Trump. But they can also be seen as the rhetorical device of mockery. To avoid committing the Straw Person fallacy, we need to use Trump’s actual statements and attacking him for advocating drinking bleach would be an error.

While Trump does not directly tell people inject disinfectants, he can be seen as engaging in a form of innuendo, a rhetorical technique in which something is suggested or implied without directly saying it. Anyone who understands the basics of how language and influence works would get that Trump’s remarks would cause some people to believe that this was something worth considering. There is evidence for this in the form of calls to New York City poison control centers and similar calls in Maryland and other states. A feature of innuendo is that it allows a person to deny they said what they implied or suggested, after all, they did not directly say it. Holding someone accountable requires having adequate evidence that they intended what their words implied or suggested. Doing this can be challenging since it requires insights into their character and motives. There is also a moral issue here about the responsibility of influential people to take care in what they say, something that goes beyond critical thinking and into ethics. But a president needs to be careful in what they say.

Trump used words indicating he thought medical doctors should test injecting disinfectants into peoples’ lungs as a possible treatment for COVID-19. Given Trump’s well-established record of dangerous ignorance, interpreting his words as meaning what they state does meet the conditions of the principle of charity and the principle of plausibility: these are his exact words, in context and with full consideration of the source.

Some of Trump’s defenders also tried to use what could be called the Steel Person fallacy. The Steel Person fallacy involves ignoring a person’s claim or argument and substituting a better one in its place.  This sort of “reasoning” has the following pattern:

 

Premise 1: Person A makes claim or argument X.

Premise 2: Person B presents Y (a better version of X).

Premise 3: Person B defends Y.

Conclusion: Therefore, X is true/correct/good.

 

This sort of “reasoning” is fallacious because presenting and defending a better version of a claim or argument does not show the original is good. A Steel Person can be effective because people often do not know the real claim or argument being defended. The fallacy is especially effective when the Steel Person matches the audience’s positive biases or stereotypes, they will feel the improved version is the real version and accept it. The difference between applying the principle of charity and committing a Steel Person fallacy lies in the intention: the principle of charity is aimed at being fair, the Steel Person fallacy is aimed at making a person’s claim or argument appear much better and so is an attempt at deceit.

While this fallacy is generally aimed at an audience, it can also be self-inflicted: a person can unwittingly make a Steel Person out of a claim or argument. This can be done in error (perhaps due to ignorance) or due to the influence of positive biases. The defense against a Steel Man, self-inflicted or not, is to take care to get a person’s claim or argument right and to apply the principle of plausibility.

In the case of Trump, he was clearly expressing interest in injecting disinfectants into the human body. Some of his defenders created a Steel Man version of his claims, contending that what he really was doing was presenting new information about using light, heat and disinfectant killing the virus. To conclude that Trump was right because of this better version would be an error in logic. While light, heat and disinfectant will destroy the virus, Trump’s claim was about injecting disinfectant into the human body—which, while not telling people to drink bleach, is a dangerously wrong claim.

Trump himself undermined these defenders by saying “I was asking a question sarcastically to reporters like you just to see what would happen.” If this is true, then his defenders’ claims that he was not talking about injecting disinfectant would be false. He cannot have been saying something dangerously crazy to troll the press and making a true and rational claim about cleaning surfaces.  Trump seems to have  used a rhetorical device popular with the right (although anyone can use it). This method could be called the “just kidding” technique and can be put in the meme terms “for the lulz.”

One version of the “just kidding” tactic occurs when a person says something that is racist, bigoted, sexist, or otherwise awful and does not get the positive response they expected. The person’s “defense” is that they did not really mean what they said, they were “just kidding.” As a rhetorical technique, it is an evasive maneuver designed to avoid accountability. The defense against this tactic is to assess whether the person was plausibly kidding. Did they intend to be funny without malicious intent and fail or are they trying to weasel out of accountability? This can be difficult to sort out, since you need to have some insight into the person’s motives, character and so on.

Another version of the “just kidding” tactic is similar to the “I meant to do that” tactic. When someone does something embarrassing or stupid, they will often try to reduce humiliation by claiming they intended to do it. In Trump’s injection case, he claimed he intended to say what he said and that he was being sarcastic. He meant to do it but was just kidding.

If Trump was just kidding, he thought it was a good idea to troll the media during a pandemic—which is a matter for ethics rather than critical thinking. If he was not kidding, then he was attempting to avoid accountability for his claim, which is the point of this tactic. The defense against this tactic is to assess whether the person was plausibly kidding, that is, did they really mean to do it and what they meant to do was just kidding? This requires having some insight into the person’s character and motives as well as considering the context. In the case of Trump, the video shows him addressing his remarks to the experts rather than the press and he seems completely serious. There is also the fact that a president engaged in a briefing on a pandemic should be serious rather than sarcastic. As such, he does not seem to be kidding. But Trump put himself in a dilemma of awfulness: he was either seriously suggesting a dangerous idea or trying to troll the press during a briefing on a pandemic that was killing thousands of Americans. Either way, that would be terrible. While this essay focuses on Trump and the COVID-19 pandemic; we should expect something similar, if not worse, should another pandemic arise during Trump’s reign.

During the COVID-19 pandemic, some politicians argued that America should be reopened because the dire predictions about COVID turned out to be wrong. On the face of it, this appears to be good reasoning that we could use in the next pandemic: if things are not as bad as predicted, we can start reopening sooner than predicted. To use an analogy, if a fire was predicted to destroy your house, but it only burned your garage, then it would make sense to move back in and rebuild the garage. While this reasoning is appealing, it also can be a trap. Here is how the trap works.

Some politicians and pundits pointed out that the dire predictions about COVID did not come true. For example, the governor of Florida said that since the hospitals were not overwhelmed as predicted, it was a good idea for them to return to profitable elective surgery. He also, like some other Republican governors, wanted to reopen very quickly. This reasoning seemed sensible: the pandemic was not as bad as predicted, so we can quickly reopen. There are also those who sneered at the dire predictions and were upset at what they saw as excessive precautions. This can also seem sensible: the experts predicted a terrible outcome for COVID-19, but they were wrong. We overreacted and should have rolled back the precautions when the predictions did not come true. But would this be a wise strategy for the next pandemic?

While it is reasonable to consider whether the precautions were excessive, there is a tempting fallacy that needs to be avoided. This is the prediction fallacy. It occurs when someone uncritically rejects a prediction and responses to that prediction when the outcome of a prediction turns out to be false. The error in the logic occurs because the person fails to consider what should be obvious: if a prediction is responded to effectively, then the prediction is going to be “wrong.” The form of the fallacy is this:

 

Premise 1: Prediction P predicted X if we do not do R.

Premise 2: Response R was taken based on prediction P.

Premise 3: X did not happen, so prediction P is wrong.

Conclusion: We should not have taken Response R (or no longer need to take Response R).

 

To use a concrete example:

 

Premise 1: Experts predicted that the hospitals in Florida would be overwhelmed if we did not respond with social distancing and other precautions.

Premise 2: People responded to this prediction with social distancing and other precautions.

Premise 3: The hospitals in Florida were not overwhelmed, so the prediction was wrong.

Conclusion: The response was excessive, and we no longer need these precautions.

 

 

While it is (obviously) true that a prediction that turns out to be wrong is wrong, the error is uncritically concluding that this proves that the response based on the prediction need not have been taken (or that we no longer need to keep responding in this way). The prediction assumes we do not respond (or do not respond a certain way) and the response is to address the prediction. If the response is effective, then the predicted outcome would not occur and that is the point of responding. To reason that the “failure” of the prediction shows that the response was mistaken or no longer needed would be a mistake in reasoning. You could be right; but you need to do more than to point to the failed prediction.

As a silly, but effective analogy, imagine we are driving towards a cliff. You make the prediction that if we keep going, we will go off the cliff and die. So, I turn the wheel and avoid the cliff. If backseat Billy gets angry and says that there was no reason to turn the wheel or that I should turn it back because we did not die in a fiery explosion, Billy is falling for this fallacy. After all, if we did not turn, then we would have died. And if we turn back too soon, then we die.

The same applied to COVID-19: by responding effectively to dire predictions, we changed the outcome and the predictions turned out to be wrong. But to infer that the responses were excessive or that we should stop now simply because the results were not as dire as predicted would be an error.

This is not to deny what is obviously true: it is possible to overreact to a pandemic. But making decisions based on the prediction fallacy is a bad idea. There is also another version of this fallacy.

A variation of this fallacy involves inferring the prediction was a bad one because it turned out to be wrong:

 

Premise 1: Prediction P predicted X if we do not do R.

Premise 2: Response R was taken based on prediction P.

Premise 3: X did not happen.

Conclusion: The prediction was wrong about X occurring if we did not do R.

 

While the prediction turned out to be wrong in the sense that the predicted outcome did not occur, this does not disprove the prediction that X would occur without the response when the response occurred. Going back to the car analogy, the prediction that we would die if we drove off the cliff if we do not turn is not disproven if we turn and then do not die. In fact, that is the result we want.

Getting back to COVID-19, the predictions made about what could occur if we did nothing are not disproven by the fact that they did not come true when we did something. So, to infer that these predictions must have been wrong in predicting what would occur if we did nothing would be an error. We do, of course, rationally assess predictions based on outcomes but this assessment should not ignore considering the effect of the response. Sorting out such counterfactual predictions is hard. In complex cases we can probably never prove what would have happened, but good methods can guide us here, which is why we need to go with science and math rather than hunches and feelings.

This fallacy draws considerable force from psychological factors, especially in the case of COVID-19. The response that was taken to the virus came with a high cost and we wanted things to get back to normal—so, ironically, the success of the response made us feel that we could stop quickly or that we did not need such a response.  As always, bad reasoning can lead to bad consequences and in a pandemic, it can hurt and even kill many people.

Stay safe and I will see you in the future.