Continuing to look at ways to apply critical-thinking principles to ethical dilemmas, including those making up national cases for this year's Ethics Bowl, what role might the identification of fallacies play when navigating tough moral choices?
A series of LogicCheck posts talked about fallacies as breaks in logic that can weaken or ruin an argument. Formal fallacies break the logical structure of an argument (as in “All dogs are animals, Francine is a dog, therefore all dogs are Francine.”), errors that do the same damage whether you are arguing over real things (like dogs and animals), pretend things (like leprechauns and mermaids), or abstractions (like As and Bs).
In contrast, informal fallacies arise from errors in the language we use in the statements making up the premises and conclusion of an argument. Since so much can go wrong with what we say, there turn to be many more informal fallacies than formal ones, a number of which arise from errors interpreting causation (that’s a fancy way of saying what causes something to happen).
For example, a fallacy I mention here and here is called post hoc ergo propter hoc (“after, therefore caused by”) in which something preceding something else is misinterpreted as having caused that something else. The rooster who assumes that because his crowing always precedes the sun rising, his crowing is the cause of the sun to rise is an example of this fallacy, as are assumptions that anything occurring after a President is elected (including things he or she had no control over, like revolutions in other countries or natural disasters) are the President’s fault.
In these cases, causation errors are based on a single data point (the thing that happened first) preceding a second one (the thing that happened after). But what about situations where there is more than just one easily dismissible data point?
For example, if someone throws a lot of parties where drinking occurs and this leads to inappropriate jokes (bordering on harassment), irresponsible sex, and reckless driving that happen during or just after those parties, there is a direct correlation between one thing (parties) and other things (harassment, irresponsible sex, and dangerous driving).
This is the situation we find ourselves in with Case #3 (“Drinking Dilemma”) in this year’s Ethics Bowl scenarios, and it seems pretty safe to say that the parties did not just coincidentally lead to irresponsible or bad behavior but caused them (or at least partially caused them). Why? Because the scenario also specifies that those parties included a factor (freely available alcohol) that is widely known to lead to risky behaviors.
While those connections are fairly clear, mistaking correlation for causation is one of the most common errors people make in their reasoning. The web site Spurious Correlations showcases ridiculous connections that nevertheless correlate quite well (like per capita consumption of margarine and the divorce rate in Maine, or the ludicrous correlation illustrated at the top of this post). But in serious cases where we are trying to determine if one thing does indeed cause another, we need to be on the lookout for potential third factors (called confounding variables) that might be playing a role.
For example, death by drowning correlates quite well with ice cream sales. So should we ban ice cream to minimize the drowning hazard it correlates with? Probably not, especially once you realize that ice cream sales correlate with hot weather as do visits to beaches and pools where drownings occur. Thus hot weather is the confounding variable responsible for both phenomena (ice cream sales and drowning), rather than one phenomenon (ice cream sales) causing the other (drowning).
Many scientific studies begin with experiments designed to show a correlation between one thing and another, with further experiments designed to eliminate alternatives to a direct connection between the two. This allows the idea that the first thing causes the second to move from a hypothesis (which can potentially be explained by the presence of confounding variables) to a better supported theory in which those confounding factors have been eliminated as a potential source of explanation.
Getting back to Ethics Bowl, Case #7 (“Don’t Check That Box”) talks about a number of reasons why students are refusing to specify their race on their college applications. This includes Asian students who claim they are being discriminated against in the college application process (especially at elite schools) due to their race.
This is a highly contentious issue that has led to at least one high-profile lawsuit in which evidence of correlation between race (Asian) and lower rates of college admission seems pretty clear. But does that correlation mean we have an unambiguous explanation of a cause, in this case anti-Asian bigotry?
During the trial over that aforementioned lawsuit, the correlation between race and admissions was not dismissed as mere coincidence but was instead explained as the result of third factors that go into college admissions decisions, including principles that consider things other than the qualification of applicants, like the need to create a diverse student body.
Now some of that diversity involves making sure traditionally unrepresented groups are represented on campus (affirmative action), but some of it involves saving spaces for the children of alumni (legacy admission).
I’m guessing that most readers have opinions about how one or both of these practices should be balanced with meritocracy (only admitting the most qualified). But notice that we are dealing not with correlations and causations, but with ethical dilemmas that arise from the need to balance competing goods.
Any choice we make when faced with distributing a limited resource (like admission slots at Harvard) is going to involve legitimate trade-offs that can be reasonably seen as unfair by the parties they shortchange. Debates over this topic (which will be taking place at Ethics Bowl events around the country) should not avoid dealing with those trade-offs (by, for example, automatically ascribing their discriminatory correlation to the cause of bigotry) but should instead grapple with how to navigate tough decisions, the results of which are going to be unfair to someone.