Jussie Smollett, Bad Inferences, and Narrative

Introduction
I've been seeing what I take to be a lot of bad inferences by smart people concerning the Jussie Smollet hoax. There is a long-running narrative on parts of the right (particularly online) that we should be skeptical of the authenticity of many hate crimes. The Jussie Smollet hoax is pouring gasoline on this narrative and spreading it outside its usual domain on the right.

While both the hoax and the narrative are ugly, this is a beautiful opportunity to talk about some of my favorite critical thinking concepts....

Key Concepts
Fallacy of Confirming Evidence: Sister of confirmation bias, the fallacy of confirming evidence is when we count only confirming evidence and ignore disconfirming evidence when forming our conclusions. For example, suppose I hold the belief that vaccines cause autism. I go out into the world and I see an autistic child and I find out that child was also vaccinated. Hypothesis confirmed! I see another child with autism that was vaccinated. Yet more evidence. Ah ha! Vaccines cause autism. I could do this all day long: Find autistic children, discover their vaccination status, and if its positive count it as confirmation for my hypothesis.

The obvious error is that I'm not taking into account all the children who have been vaccinated but aren't autistic. In general terms, I'm only taking into account positive evidence and ignoring disconfirming evidence as I form my view.

The fallacy of confirming evidence often works together with motivated reasoning. Rather than examine a data set then come to a conclusion, I begin with the conclusion, "vaccines cause autism", then go out into the world and carefully select only the evidence that supports this view.

Good reasoning requires that we take into account both confirming and disconfirming evidence. Which leads to our next concept...

Framing: Absolute Numbers vs Rates: It's very easy to mislead people with absolute numbers since they provide no context. For example, if you hear that 20 people got A's in my class last semester you might think my class is easy. But not so fast. To make the correct evaluation you need to know how many people were in my class total. If there were only 20 students in my class then 20 A's is a decent indication that either my class is easy or I'm the world's greatest teacher. However, if it turns out that I had 500 students in my class, then you might draw different conclusions.

The lesson here is that we cannot evaluate absolute numbers without context and using rates is an excellent way of giving context. Partisan media and groups often use absolute numbers as a way of creating a narrative.

There are a bunch more, but this should be enough to get the party started. I've listed some other ones at the end of this post for the keeners.

The Jussie Smollet Hoax and Hate Crime Hoaxes
With these critical thinking concepts in our back pocket, let's take a look at hate crime hoaxes. Several right-wing media outlets have helpfully compiled lists of all the hate crime hoaxes during the Trump presidency going back to 2016. These lists are graciously prepared in order to save us from the epidemic of liberal hate crime hoaxes aimed to delegitimize the moral bonafides of Trump and his supporters.

I counted about 20 on the list. Let's triple that for fun. That's 60 hoax hate crimes since 2016. That makes 20/year!!!! OMG we're over-run with hate crime hoaxes. All hate crimes must be hoaxes. #DontBelieveThem

Oh, wait. We need to know the total number of reported hate crimes/year. The FBI puts it at around 7000/year. Let's do some math: Let's see...7000/20....that's 0.286%. So, less that one percent of reported hate crimes are hoaxes (if we triple the actual number). Clearly this is an epidemic. Our immediate reaction to someone claiming to be the victim of a hate crime should be to disbelieve them because there's a .286% chance it's a hoax:

Of course, there's a 99% chance that it isn't but let's not let statistics interfere with the narrative folks! Let's also keep in mind that the FBI and other reporting agencies estimate that the number of actual hate crimes is much higher than the number that actually get reported. This means that the percentage of hate crimes that are hoaxes is probably even lower than 2/10th of a percent.

As a final note, suppose absolutely everyone who was subject to a hate crime is included in the FBI statistics (which is very unlikely since the groups who are typically subject to hate crimes have good reasons to fear the police). Suppose we also multiply the actual incidence of confirmed hoaxes by TEN. That would be 20x10=200 hate crime hoaxes since 2016. Which means ~67 hoaxes per year. 7000/67= ~1%. So, even in the most charitable interpretation of the hate crime hoax epidemic, the incidence rate doesn't rise above 1%.

Don't fall for the right-wing narrative. Remember, facts not feelings!

Bonus Round:
Availability Bias: This is the tendency to think that the examples that most easily come to mind are also the most representative examples of a phenomena. The availability bias explains why many people are afraid of flying. When there's an airplane accident it's all over the news. We don't hear major news reports of all the airplanes that didn't crash. So, when some people think of airplane safety the first thing that comes to mind is the crashes, not the same flights. Because these are the examples that most readily come to mind, the mind takes them to be the most representative cases of airplane safety.

In the case of hoaxes, we are inundated with stories if there is a hoax (especially if you are in a right wing media ecosystem). The 7000 legitimate cases rarely get the media coverage the hoaxes do. Since the hoaxes are the most available cases, the mind takes them as the most representative cases, and extrapolates from them general conclusions about hate crimes.

Selection Bias: A selection bias will operate in conjunction with the availability bias. Which sorts of cases are the most likely to make the news? The ones that are outliers for a variety of reasons. They often involve high profile people or are anomalous for various reasons. There are 7000 hate crimes per year. Why don't we see all of them reported? Why doesn't right wing media report all the actual cases? There's selection bias going on. That media will only pick up the ones that serve to fulfill a narrative.

Another selection bias is that those who commit hate crime hoaxes are most likely to do it for attention. They want to get noticed. Hence, these types of cases will disproportionately enter the media cycle.

Base Rate Neglect/Base Rate Fallacy: This one's a bit tricky to explain so I'll hand over the details to the wikipedia article. Suppose the incidence rate of a phenomena is low. For example, 1% of all hate crimes are hoaxes . That means that for every case, all things being equal, we should assume that there's a 1% chance that it's a hoax. However, people fixate on the particulars of each case ignoring the base rate. It's not that particulars don't matter, it's that people place too much weight on the particulars in their reasoning while putting too little on the base rate.

Critical Thinking: BS Detectors on Stun. How to Detect Illegitimate Biases

Introduction
In this section we are going to start learn how to detect BS.  Lets move beyond the general notion of 'bias' and get more specific about biases and how the affect the strength and validity of arguments.  Recall that one way we can classify biases is according to how much skin the arguer has in the game; that is, the degree to which the arguer stands to gain from his audience accepting his position.  In this respect we can make 3 broad categories of bias: legitimate, illegitimate, and conflict of interest.  By now you should be able to say something about each type.  Moving on...

Confirmation Bias:
Another way to classify bias in an argument is according to how the information is presented.  One of the most familiar biases is confirmation bias.  Confirmation bias is when we only report the "hits" and ignore the "misses"; in other words, we only include information/evidence/reasons in our argument that support our position and we ignore information that disconfirms.  Confirmation bias is often (but not always) unintentional and everyone does it to some degree (except me).

What?  You don't think you do?  Oh, I get it.  You're special.  Ok, smarty pants.  Here's a test.  Lets see how smart you are.  And don't forget you've already been give fair warning of what's going to happen. The smart money says you will still fall into the trap.

Click on this link and do the test before you continue:
http://hosted.xamai.ca/confbias/index.php
.
.
.
.
.
I said do the test first!
.
.
.
.
.
.
Well?  Vas happened? I'm going to continue with the assumption that you committed the confirmation bias.  Hey, don't feel bad--we're hardwired for it.  Before we move forward and discuss how and why confirmation bias works, let me take you on a philosophical aside.

Aside on Falsificationism
I promised myself I wouldn't do this but it'd be helpful to bring in a little philosophy here.  Please meet my good friend Carl Popper (no relation to the inventor of the popular snack food known as Jalepeno Poppers).

Popper made a very important philosophical observation in regards to how we can test a hypothesis:  he said we cannot test for a hypothesis' truth, rather we can only test for its falsity.  This is called falsificationism.  In other words, there are infinitely many ways to show that a hypothesis is true, but it only requires one to show that it is false.  We should focus on looking to falsify rather than to confirm.

In technical philosophy we refer to an instance of a falsification as a counter example.  A counter example is a case in which all the premises are true but the conclusion is false (more on this later).

For illustrative purposes lets apply this principle to the number-pattern test from the link.  You were given a series of numbers and asked to identify the principle that describes the pattern.  Suppose, (unbeknownst to you) the ordering principle is any 3 numbers in ascending order.  How did you go about trying to discover the ordering principle? You looked at the numbers and like most people thought it was something to do with even numbers evenly spaced.  You looked at the sample pattern and tried to make patterns that conformed to your hypothesis.

For instance, if the initial pattern was 2, 4, 6 you might have thought, "ah ha! the pattern is successive even numbers!"  So, you tested your hypothesis with 8, 10, 12.  The "game" replied, yes, this matches the pattern.  Now you have confirmation of your hypothesis that the pattern is successive even numbers.  Next, you want to further confirm you hypothesis so you guess 20, 22, 24.  Further confirmation again!  Wow! You are definitely right!  Now, you plug you hypothesis (successive even numbers) into the game, but it says you are wrong.  What?  But I just had 2 instances where my hypothesis was confirmed?!

Back to Confirmation Bias
Here's the dealy-yo.  You can confirm your hypothesis until the cows come home.  That is, there are infinitely many ways to confirm the hypothesis.   However, as Popper noted, what you need to do is to ask questions that will falsify possible hypotheses.  So, instead of testing number patterns that confirm what you think the pattern is, you should test number sequences that would prove your hypothesis to be false.  That is, instead of plugging in more instances of successive even numbers you should see how the game responds to different types of sequences like 3, 4, 5 or 12, 4, 78.  If these are accepted too, then you know your (initial) hypothesis is false.

Lets look at this from the point of view of counter examples.  Is it possible that all our number strings {2, 4, 6}, {8, 10, 12}, {20, 22, 24} are true (i.e., conform to the actual principle--ascending order) but our conclusion is false (i.e., the ordering principle is sequential even numbers).  The answer is 'yes', so we have a counter-example.  In other words, it's possible for all the premises to be true (the number strings) yet for our conclusion to be false.  

How do we know our premises can be true and the conclusion false?  Because our selected number stings are also consistent with the actual ordering principle (3 numbers in ascending order).  If this is the case (and it is), all of the premises are true and our conclusion (our hypothesis) is false.  We have a counter example and should therefore reject (or in some cases further test) our hypothesis.

If you test sequences by trying to find counter examples you can eventually arrive at the correct ordering principle, but if you only test hypothesis that further confirm your existing hypothesis, you can never encounter the necessary evidence that leads you to reject it.  If you never reject your incorrect hypothesis, you'll never get to the right one! Ah!  It seems sooooooo simple when you have the answer!

Why do we care about all this as critical thinkers?
When most arguments are presented, they are presented with evidence.  However, (usually) the evidence that is presented is only confirming evidence.  But as we know from the number-pattern example, the evidence can support any number of hypothesis.  To identify the best hypothesis we need to try to disconfirm as many hypotheses as possible.  In other words, we need to look for evidence that can make our hypothesis false.  The hypothesis that stands up best to falsification attempts has the highest (provisional) likelihood of being true.

As critical thinkers, when we evaluate evidence, we should look to see not only if the arguer has made an effort to show why the evidence supports their hypothesis and not another, but also what attempt has been made to prove their own argument false.  We should also be aware of this confirmation bias in our own arguments.

Bonus Round:  Where do we often see confirmation bias?
Conspiracy theories and alt-med are rife with confirmation bias.  Evidence is only used that supports the hypothesis.  Alternative accounts of the results are not considered and there is often no attempt to falsify the pet hypothesis.

Confirmation Bias and the Scientific Method:
We'll discuss the scientific method in more detail later in the course but a couple of notes are relevant for now.  The scientific method endeavors to guard against confirmation bias (although, just as in any human enterprise, it sometimes creeps in).  There are specific procedures and protocols to minimize its effect.  Here are a few:

• When a scientist (in a lab coat) publishes an article, it is made available to a community of peers for criticism.  (Peer review)
• Double blinding
• Control Group
• Incentives for proving competing hypotheses and theories wrong (be famous!)
• Use of statistical methods to evaluate correlation vs causation

Confirmation Bias 2:  Slanting by Omission and Distortion
Slanting by omission and distortion are 2 other species of confirmation bias.  Slanting by omission, as you might have guessed, is when important information is left out of an argument to create a favorable bias.

Perhaps a contemporary example can be found in the gun-rights debate.   We often hear something like "my right to bear arms is in the Constitution."  While this is true, the statement omits the first clause of the Second Amendment which qualifies the second, i.e., that the right to bear arms arises out of the historical need for national self-defense.  The Constitution is mute on the right to bear arms for personal security.  There also the troublesome word "well-regulated".

Omitting these fact slants the bias in favor of an argument for an unregulated right to bear arms based on personal self-defense.  This may or may not be a desirable right to have, but it is an open question as to whether this right is constitutionally grounded.

Another example of slanting by omission might be the popular portrayal by the media of terrorists in the US of being of foreign origins.  Such an argument omits many contemporary acts of domestic terrorism perpetrated by white American males (for example, Ted Kaczynski aka the unibomber and Timothy McVeigh).

Slanting by distortion is when opposing arguments/reasons/evidence are distorted in such as way as to make them seem weaker or less important than they actually are.  Think of slanting by distortion as something like white lies.

For example, famously, when Bill Clinton said "[he] did not have sexual relations with that woman," he was slanting by distortion in the way he deceptively used the term 'sexual relations'.

Summary

• A common type of bias is confirmation bias in which only confirming evidence and reasons are cited, and falsifying evidence is ignored.  
• A good way to test a hypothesis or argument is to ask whether it's possible for all the premises to true and the conclusion to be false; that is, are there counter examples.  Instead of emphasizing confirming evidence, a good argument also tries to show why counter examples fail.  In other words, it shows why, if all the premises are true we must also accept the particular conclusion rather than another one.  
• As critical thinkers assessing other arguments, we should try to come up with counter examples.
• Slanting by omission is when important information (relative to the conclusion) is left out of an argument.
• Slanting by distortion is when opponents arguments/evidence are unfairly trivialized.