Logical Fallacies in the Time of COVID

Logical Fallacies in the Time of COVID
By Kristen Panthagani

As I’ve gone into the world of facebook comments, I have noticed that many people trying to make sense of the pandemic (myself included) are running into a number of logical fallacies. As you listen to other people’s views and form your own, try to keep these fallacies in mind to avoid common but unhelpful missteps in human reasoning.


First, what are logical fallacies? My good friend, Dr. Gretchen Ellefson (Assistant Professor of Philosophy) just taught a class on critical thinking that included this very topic! Here is her basic intro to fallacies:


First, at a basic level, fallacies are flaws in reasoning or faulty forms of reasoning. What we mean when we say this is that people have arguments in mind when they form beliefs, meaning that they have reasons supporting a given conclusion. An argument has a fallacy when the reasons fail to do their job–they don’t actually support the conclusion.


The second thing is that fallacies are super normal and common and everyone uses them. The reason we want to talk about them is because recognizing where fallacies come in can help us do better at making sure we have good reasons for what we think is true. This means that when we see people using fallacies, we should recognize that this is a normal human thing, not a sign of stupidity.


That said, fallacies do tend to track forms of reasoning that human brains, for whatever reason, find compelling, even though they shouldn’t be. This means that fallacies CAN be used in really manipulative ways. If someone wants to convince their audience of something, and they know that there aren’t good reasons for their audience to accept that thing, then they may look for ways to use these kinds of patterns of reasoning which people will be less likely to immediately recognized as flawed.


Fallacy 1: Some people who have stayed home from work ended up getting COVID-19, therefore staying home doesn’t help prevent the spread of COVID-19.

This is a false dilemma fallacy — where the arguer tries to make it seem there are only two options (staying home protects 100% of people or it doesn’t protect at all), when in fact other explanations exist. In this case, an alternative explanation exists: staying home helps slow the spread of COVID-19 so that fewer people get sick, but it is not a guarantee that no one will get sick. (This also has elements of the false cause fallacy).


Fallacy 2: I have been around lots of people and I haven’t gotten sick, therefore social distancing isn’t really needed.

This is the anecdotal fallacy which assumes that one person’s experience must be reflective of everyone in the population. A similar argument could be made for drunk driving; someone could say that because they drove drunk last night and nobody got hurt, avoiding drunk driving is not necessary. Obviously that is not true — most would realize that this person was just lucky that they didn’t cause an accident. We have to look at the effect of both social distancing and drunk driving on the population, not just individual cases. Why? Because a lot of people who are exposed to the coronavirus do not end up getting symptoms, so if you are one of those lucky people, you may be around tons of people (and get exposed) and never get sick. Or maybe you have been lucky and just haven’t been exposed yet, even if you’ve been around lots of people. This does not mean that we can assume that will be the case for everybody. (This also has elements of the false cause fallacy).


Fallacy 3: If someone is concerned about the economy, they must not be taking the pandemic seriously. And alternatively, if someone is advocating for social distancing, they must not care about the economy.

These are also false dilemma fallacies, where issues are falsely divided into simple boxes creating an either/or scenario, when reality is in fact much more complex.


Fallacy 4: There are videos of Dr. Fauci and Bill Gates discussing the likelihood of a pandemic several years ago; these videos are evidence that these men were involved in planning the pandemic.

This is a false cause fallacy (also perhaps a post hoc fallacy), which assumes that if two events are associated, one must be the cause of the other. A similar but more obvious fallacy would be an oncologist giving a cancer prognosis that turns out to be accurate, and then suspecting them of murder because they “knew” when the patient was going to die. Or accusing meteorologists of manipulating hurricanes because they accurately predicted it was going to hit a major city. Science can be used to predict lots of things (with varying degrees of certainty), including the possibility of a pandemic.


Fallacy 5: If the government forces us to wear masks for the sake of public health, they will soon encroach on our liberty in more extreme ways.

This is the slippery slope fallacy, which avoids discussing the issue at hand (mandatory masks) by distracting with a far more extreme claim, and assuming that these two things must be connected without providing any evidence that they are connected.


Fallacy 6: Don’t worry about the civil liberty implications of shut downs, think about all the people who are suffering from the virus right now!

This is an appeal to emotion fallacy, where instead of providing a logical argument, the arguer attempts to manipulate the person’s emotions.


Fallacy 7: The vast majority of people support social distancing, therefore it’s the right thing to do. This is the bandwagon fallacy, which asserts something is true because lots of people think it’s true. This is, of course, not how reality works; there have been many examples where popular opinion was flat out wrong.


Fallacy 8: Because the last argument (Fallacy 7) was a fallacy, social distancing is not the right thing to do.

This is a fallacy fallacy, which asserts that any conclusion that is based on a fallacy must be wrong. This isn’t necessarily the case; it just means that the statement didn’t pull from valid evidence or arguments, but it may be the case that valid arguments do exist.


Fallacy 9: Getting immunity naturally is better than getting a vaccine because vaccines are artificial.

This is the appeal to nature fallacy, which asserts that because there are so many good things that come from nature, everything from nature is always better than something that is created by humans. This is of course untrue, there are many bad things that come from nature (like hurricanes, and viruses) and many good things that are man-made (like breakfast tacos).


Fallacy 10: The person with higher scientific credentials must be right.

This is the appeal to authority fallacy, which asserts that authority figures (experts) are always right. But of course, this is not always true; reality does not bend to the will or whims of experts. Right now I think this one is especially confusing, because the scientific community says “trust the experts!” but then when an expert says something a bit weird, they say “ignore them!” The rules of science are this: the best data and the best analysis win. It does not matter who is saying it; the data and analysis are all that really matter. Often, scientists with more credentials are better at recognizing good data and analysis, so that is why it usually makes sense to “trust the experts!” But sometimes, an expert or two might latch onto bad data for a variety of reasons (they got overly attached to their hypothesis and want to be right, they have a financial conflict of interest, the science conflicts with their ideological views, they need an audience, they are speaking outside their area of expertise). In these cases, it is best to ignore them. So if you don’t know how to judge the quality of the data and analysis for yourself, how do you tell the difference? My best advice is to look at what the majority of scientists are saying — there are very few things that scientists dislike more than bad science (hence the existence of this blog), and we will call it out, no matter how many degrees the person promoting it may have. The consensus of the majority of scientists (who have expertise on the question at hand) will usually* lead you to the right answer.


*There are a few cases in history where the majority of scientists were wrong and the lone “crazy one” was right. Here is one of my favorite examples.

Lessons from Graduate School for the COVID Pandemic

Lessons from Graduate School for the COVID Pandemic

By Kristen Panthagani

Data, medicine, and research have all of a sudden become part of every day life, and I have noticed the world is getting tripped up on a few lessons that every PhD student learns in their first few years of graduate school. Here is a cheat sheet to help you interpret the COVID-19 pandemic, and all the uncertainty around the data and studies we are seeing.

1. There is no such thing as perfect data.

There is only better data and worse data. I have seen people want to throw out all data on COVID-19 comparisons because none of the data is perfectly accurate. It is true that there is no database that has perfect counts of COVID-19 cases or deaths, and every plot of data you see likely has some (or many) inaccuracies in it. However, this does not mean it is useless. Some simple guidelines to follow are (1) find the most reputable data source you can, and (2) when you look at the data, take potential flaws into consideration, then (3) make the best conclusion you can with the data you have available, considering the flaws (Try “hmmm I think I see a trend here, but there is some uncertainty due to differences in testing” rather than “I know for sure that state A is doing a better job at social distancing than state B” or “this is all utter rubbish.”) This is the process used to interpret all data in science, not just rapidly emerging pandemic data. Yes… some data is so flawed that it should be thrown out because it’s trash, but if your standard for throwing out data is if it contains any flaws or inaccuracies at all, you will be throwing out pretty much every data set in existence.

2. It is easy to only pay attention to the data that supports your hypothesis (and ignore
the data that goes against your hypothesis).

It is easy, but oh so wrong. This is why I personally remained skeptical of the anecdotal evidence for hydroxychloroquine/azithromycin efficacy against COVID-19… we have heard reports from physicians saying that it does work, which is exciting. But is it possible that only the positive stories are getting circulated? If a doctor tries this drug combination and it doesn’t work, are they going to be interviewed by the evening news or shared all over facebook? Probably not. Likewise, early on there were multiple small studies on whether hydroxychloroquine/azithromycin works in humans… with conflicting results. Should we only pay attention to the studies that say it works, and ignore studies that say it doesn’t work? Nope. We have to judge each of these studies based on the quality of the study, not on whether it gives us the answer we want.

3. It is easy to get fooled by early data.

This one I think nearly every scientist has been guilty of at one time or another. You start an experiment, have a few samples, and it looks like there is an amazing result! You get so excited and want to publish immediately! You’re about to win the Nobel Prize!!! However, as you do the more robust experiments (with more samples), those exciting results start to look less exciting. This has happened to me on multiple occasions in my research (minus the Nobel Prize bit). The need for an adequate sample size (with proper controls) is a real thing… but I think it’s hard to appreciate how important this is until you’ve been fooled by your own data.

4. There is a big difference between studies done in human cells in a dish (in vitro), in
animals, and in real breathing humans.

The vast majority of treatments that look promising in a dish or in animals end up failing in humans. If you’re curious why this is (besides the obvious fact that neither cells in a dish nor mice can capture the full biological complexity of a human being), check out the book Rigor Mortis: How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Billions (it’s not nearly as depressing as that title sounds). But the tl;dr version is — wait for the human clinical trials to decide if a drug really works.

5. Scientific and medical expertise does not readily transfer fields.

An expert in physics is not the right person to critique an epidemiological model predicting how many COVID-19 cases there are going to be. Not all medical doctors are trained to do scientific research and design experiments. And a researcher (PhD) may know everything there is to know about the biology of a specific virus, but they do not know how to treat a decompensating ICU patient sick with that virus. Do your best to listen to people who are speaking out of their expertise and who know their own limitations.

6. It takes a long, long time to be sure of anything in science.

Most of the things we “know” in science are based on hundreds (if not thousands) of studies from research that has been building for decades. We scientists do get excited about a single new study that shows something surprising and new, but we always take it with a big grain of salt, because we know that no single study is perfect. Instead we rely on evidence from many studies before a scientific hypothesis is converted to a scientific fact.

UV Light and Disinfectants as COVID Treatments?

UV Light and Disinfectants as COVID Treatments?

By Kristen Panthagani

There was lots of excitement and kerfuffle surrounding the idea of using UV light or disinfectants to treat coronavirus. Here is the science behind how these things kill viruses.

UV Light

UV light causes permanent damage to genetic material (like DNA and RNA) by changing its chemical structure. If enough UV light is shined on virus particles, it will break its genetic material, meaning it can’t replicate anymore. This is why UV light works to disinfect surfaces. Does that mean it could be used as a treatment in people? No… because UV light does the same thing to human genetic material. Med school 101: severe DNA damage = cancer and/or death. Additionally, it would be very hard to deliver the UV light to the cells infected with the virus – just shining UV light on a person would not work as the skin blocks it from entering the body. It is physically impossible to dissolve light in liquids so you can’t give people a UV light IV infusion… I guess people would have to inhale nanoparticles emitting UV light if you really wanted to deliver it to the lungs.. but if they did that it would severely damage the lungs, either leading to death now or lung cancer down the road. This is why it is not possible to use UV light to treat humans… it is a potent carcinogen.

Disinfectants (Bleach, Hydrogen Peroxide)

Bleach and hydrogen peroxide are very reactive molecules that like to react with everything… they will try to rearrange the chemical structure of everything they come into contact with. This is why they’re so good at disinfecting… they will just destroy everything in their path. And this of course is why they can’t be used as a treatment for humans… if they were injected into the blood stream, they would destroy the blood vessels before getting very far… if they were swallowed, they would destroy the mouth, esophagus, and stomach… if they were somehow vaporized and inhaled, they would destroy the lungs. Bleach and hydrogen peroxide are like a wrecking ball, and antivirals are like a sharpshooter… Yes the wrecking ball might destroy the virus, but it will destroy everything else along with it. Antivirals are precise and don’t attack the healthy human cells, they only attack the virus.