Be Very, Very Quiet… I’m Hunting Fallacies
And why that might be the wrong way to think about reasoning
You can also hear AI Matt’s summary of the piece below.
Every time I teach my graduate course on ethical, evidence-based decision making, in week three an issue inevitably pops up. That’s the week when we shift into argumentation and reasoning. We talk about deductive versus inductive arguments. We talk about inference strength. And yes, we talk about logical fallacies. I usually introduce a handful — affirming the consequent, slippery slope, argument from authority. Nothing exotic. Just the classics.
And without fail, at least one student poses a question on the discussion board.
“So… isn’t this whole course just one big argument from authority?”
It’s not a bad question. In fact, it’s a perfectly reasonable one — especially after I’ve just explained that appeals to authority are supposedly fallacious.
What that exchange eventually forced me to reconsider wasn’t whether appeals to authority are fallacious in some technical sense. It was something more practical: when does calling something a fallacy actually undermine an argument — and when does it simply end the conversation prematurely?
From a strict deductive standpoint, the student has a point. A deductive argument requires that the conclusion necessarily follows from the premises. “Expert A says X” does not entail “Therefore, X must be true.” Even if you add Experts B, C, and D who all agree, the conclusion still doesn’t follow with logical certainty. Authority — no matter how many layers you stack — never produces inevitability.
So I’m left with a choice. I can concede the deductive point and move on. Or I can explain why real-world decision making rarely operates in deductive space at all.
For years, that explanation felt sufficient. New class, same exchange. Rinse and repeat.
But over time, I began to recognize that the student’s question wasn’t really the issue. The issue was that I had framed fallacies as if they operated categorically — as if identifying one was a refutation of an argument. That framing practically invites the objection.
Perhaps some fallacies — maybe even most — aren’t reasoning errors in any universal sense. Maybe they’re recurring patterns of inference whose strength depends on context. Boudry (2026) recently made a similar point, arguing that fallacies mostly exist in textbooks1.
If that’s even partly right, then the function of a fallacy label changes. In a world where most reasoning is inductive — probabilistic, defeasible, context-bound — we can’t treat these reasoniong patterns as automatic refutations. Their presence just means we should look more closely at how far the conclusion is stretching beyond what the premises can actually support.
That doesn’t make fallacies irrelevant. It just changes what they’re for. The issue isn’t whether a reasoning pattern appears on a list. The issue is how well calibrated the inference actually is.
From Fallacy Hunting to Inference Calibration
A while back, I wrote about the illusion of perfect reasoning — the tendency to expect certainty from probabilistic arguments. I argued that most real-world reasoning is inductive, and that the right question isn’t whether a conclusion is guaranteed, but whether it is probably true (i.e., reasonable). The anecdote I mentioned at the beginning highlights this issue. Students often try to reposition what is essentially an inductive claim into a deductive one.
And that’s where the fallacy language starts to do something subtle. Once we introduce a label like “argument from authority,” the conversation shifts toward classification. Is this a fallacy or not? But that question only really makes sense if we’re operating in deductive space. In inductive space, the issue isn’t structural invalidity. It’s probabilistic strength. How confident can we be in a given conclusion given what we know?
One of the errors I mentioned in my earlier reasoning piece was “committing logical fallacies.” In retrospect, that framing wasn’t quite right. I still think weak reasoning patterns exist. But what I didn’t fully articulate is that the deeper problem is often that we commit an inferential overleap — we allow the conclusion to outrun the strength of the premises supporting it2.
When an argument is intended to be deductive, that overleap is fatal. If the conclusion doesn’t follow necessarily, the argument fails on its own terms. When the argument is inductive, however, the issue is one of proportion. The question isn’t whether the conclusion follows with certainty. It’s whether the premises justify the level of confidence being claimed in the conclusion. In both cases, the underlying problem is the same: claiming more than the premises can support.
Take the slippery slope. It’s often introduced as a classic fallacy: If we allow A, it will inevitably lead to B, then C, and eventually disaster Z. The problem, we’re told, is that the progression is asserted without justification.
That’s fair — if inevitability is the claim.
But most real-world slope arguments aren’t about inevitability. They’re about likelihood. Allowing A may increase the probability of B because it shifts incentives, establishes precedent, or lowers resistance to the next step. Once you move into that probabilistic space, the question changes. It’s no longer “Is this a fallacy?” It’s “How strong is the case that A meaningfully increases the chance of B?”
Weak slope arguments assume escalation. Stronger ones show why escalation becomes more likely.
But there’s another piece that often gets overlooked. Even if allowing A meaningfully increases the probability of B, that doesn’t automatically make catastrophe Z likely. Each additional step in the chain introduces new contingencies and new decision points. The probability that A leads to B might be moderate. The probability that A ultimately leads to Z depends on every intermediate link holding. With each added step, uncertainty compounds.
In other words, the longer the slope, the thinner the connection between the first step and the final outcome. And the more cautious we should be about the confidence we attach to it.
This is where the fallacy label can be useful — and where it can mislead. It’s useful when it draws attention to an unjustified leap from possibility to inevitability. It misleads when it treats any probabilistic argument as structurally flawed. The label doesn’t undermine the argument. Demonstrating the inferential gap does.
I could run the same exercise with several entries on the standard fallacy list. In his original piece, Boudry does exactly that. What you begin to see is that, in many cases, the pattern itself doesn’t automatically weaken an argument. What weakens it is something more specific — the size of the inferential gap between what the premises support and the confidence the conclusion demands.
Appeals to authority aren’t inherently weak. Slopes aren’t inherently exaggerated. Generalizing from data isn’t inherently careless. In many cases, these are ordinary features of inductive reasoning. The problem isn’t the pattern itself. It’s the size of the inferential gap.
Once you start looking at it this way, using a fallacy label as a trump card risks committing the very error it claims to diagnose — asserting more than the premises justify3.
Inductive Reasoning Still Has Standards
But none of what I’ve argued so far means weak reasoning should get a free pass. Moving away from categorical fallacy labels doesn’t eliminate standards. It just changes where and how those standards apply.
If we stop treating fallacies as automatic logical crimes, we still need a way to distinguish between strong and weak inferences. Otherwise, we risk replacing rigid classification with something equally unhelpful — logical permissiveness.
Consider one of the better-known entries on the fallacy list: cum hoc ergo propter hoc — “with this, therefore because of this.” It’s the Latin shorthand for inferring causation from mere association. In contemporary language, it’s often reduced to the slogan “correlation does not equal causation.”
As a warning, that’s useful. Observing that X and Y move together is not the same thing as demonstrating that X causes Y. Covariation is a necessary condition for causation — if two things never vary together, one cannot be causing the other. But it is also the weakest condition. Association alone tells us very little about direction, alternative explanations, or whether both variables are responding to something else entirely.
The inferential overleap occurs when correlation is treated as sufficient. The data show that X and Y are related. Therefore, X must be causing Y. That leap exceeds what the evidence can reasonably justify.
What Counts as Reasonable?
The challenge in all this is that shouting “Fallacy!” is far easier than asking whether an inference is actually reasonable — and what, exactly, we even mean by that in the first place.
In deductive reasoning, the answer is straightforward. A conclusion is reasonable if it follows necessarily from the premises. But inductive reasoning doesn’t offer that kind of clarity. It deals in degrees, not guarantees.
In inductive reasoning, reasonableness is about proportion. The confidence we’re entitled to in a conclusion should track the tightness of the inferential connection to its premises. The fewer and smaller the inferential leaps, the more confidence is warranted. The larger the leaps, the more restraint is required.
A fallacy label is useful if it reveals a mismatch — if it helps show that the conclusion claims more than the premises justify. It’s far less useful if it substitutes for that demonstration. Naming the pattern doesn’t show the inferential bridge is weak. It only signals that it might be.
That sounds simple enough. In practice, though, it requires harder work. We have to ask how tightly the conclusion follows from the premises, how much inferential distance we’ve introduced, and what plausible alternatives remain. Even more importantly, we also have to consider what’s at stake.
In low-stakes contexts, we tolerate more uncertainty. In high-stakes settings, we demand stronger justification. What counts as “reasonable” shifts with consequences, even if the underlying premises do not. These aren’t binary judgments. They’re calibrations of confidence.
And we can fail those standards in more than one way. Sometimes we overreach — the conclusion outruns what the premises can sustain. The logical connection is thin, the inferential chain stretches too far, viable alternatives remain — yet we speak with unwarranted certainty.
Other times, we don’t leap too far. We reshape the premises — intentionally or not. We weaken the opposing position — strawmanning it — so rebuttal appears decisive. We narrow the options so one outcome seems inevitable — creating a false dilemma. In those cases, the confidence attached to the conclusion is artificially high because the premises stack the deck from the outset.
Either way, the issue is the same: the conclusion carries more certainty than a fair reading of the argument earns.
Seen this way, fallacy labels don’t disappear. They stop functioning as automatic verdicts. They become prompts — signals to ask whether the inference has been calibrated honestly, whether the premises have been represented fairly, and whether the confidence attached to the conclusion is proportionate to what the premises can reasonably support.
Which brings me back to week three of my class.
When a student asks whether the course is just one big argument from authority, I used to treat that as something to untangle — as if the label itself needed to be defended against. But the label isn’t really the issue. The course does rely on authority. That’s what a course does. The question is what follows from that fact.
Does the presence of authority make the conclusions certain? Of course not. But does it increase the probability that the claims are credible — especially when independent sources converge, when reasoning is transparent, and when alternatives have been considered? Often, yes. The real work isn’t spotting authority. It’s deciding how much weight it deserves — given the quality of the evidence, the independence of the sources, and the alternatives that have been considered.
That’s harder than invoking a label. But it’s closer to what real-world reasoning actually requires — not a checklist of fallacies, but a calibrated judgment about how much confidence our inferences actually deserve.
I did largely imply this same reaosning in my discussion of it, but the framing of weak reasoning as “logical fallacy” commission needed some refinement.
In many respects, this mirrors the way “cognitive bias” is often treated as synonymous with systematic reasoning error — a treatment I discussed more fully in a prior post.
Nice write up, Matt.
We all know that induction fails as explanation; yet, every night we set an alarm to wake tomorrow having induced that the Sun will return.
Reasoning is behaviour that occurs in specific contexts with various outcomes. We've seen this 3-term contingency before. 👍