What Your Doctor Didn’t Learn in Medical School — Logical Fallacies in Everyday Medicine

A conversation with Dr. Jordan Grant, former board-certified urologist.

There’s a question so simple it barely sounds like philosophy, but once you start using it, it  can change a great deal about how you engage with expert opinion. 

How do you know that?

That’s where this conversation with Dr. Jordan Grant begins — and it’s where most honest medical conversations probably should begin too. Jordan isn’t your typical podcast guest. He spent years training in urologic surgery, left a conventional practice he’d built alongside his wife, and now spends his time doing something rather different: reading, thinking, and helping patients understand what’s actually being said when a doctor hands them a diagnosis or a prescription.

What he’s found, digging into philosophy of science and logic the way most doctors never get the chance to, is that the reasoning errors in medicine are not always rare edge cases. In his experience and observation they appear frequently enough to be worth understanding. 

The Question That Can Change Everything

Jordan describes himself as someone who was always asking why. As a kid in Sunday school, he was the one raising his hand to ask about time and eternity. As a medical student, he started noticing the same restless quality in himself — except now it was aimed at the assumptions baked into clinical practice.

The question how do you know that? belongs to a larger field called epistemology — the study of how we can justify knowing anything at all. It’s an ancient question, and medicine has largely not engaged with it as deeply as it might.

When a doctor makes a claim about your health — your cholesterol is too high, this gene puts you at risk, you need this treatment — what they’re almost never offering is proof in the strict sense. What they’re usually offering is correlation dressed up as certainty. And learning to notice the difference is one of the more useful skills you can develop.

Jordan is clear that he’s not suggesting you treat every consultation like a courtroom cross-examination. There’s a time and place. But when decisions involve your body, your health, and interventions with real consequences, the question how do you know that? is more than reasonable. It’s worth asking.

The Fallacy That’s Everywhere Once You See It

The centrepiece of this conversation is a formal logical fallacy called affirming the consequent. It sounds technical, but the structure is simple:

If A, then B. B is true. Therefore A.

The problem is that this form of reasoning is always invalid as a logical proof. B being true doesn’t confirm that A caused it. There could be dozens of other explanations for B.

Jordan first encountered this reading Bertrand Russell — not a man known for sloppy thinking — who admitted that most scientific argument ultimately rests on this kind of inductive fallacy. And once you start looking for it in medicine, it becomes difficult to overlook.

The cholesterol example is a good one. The standard claim runs roughly like this: if high cholesterol causes heart disease, then people with high cholesterol will be at greater risk of heart attacks. Some people with high cholesterol have heart attacks. Therefore, high cholesterol causes heart attacks.

But as Jordan points out, plenty of people with high cholesterol never have heart attacks. Plenty of people with low cholesterol do. In is view, the cause-and-effect story doesn’t hold up — and the ability to manipulate cholesterol levels directly in the human body to observe a result has not, to his knowledge, been clearly demonstrated.

What medicine has done instead is correlate lab numbers with health outcomes, give those correlations the weight of causation, and then build an entire intervention industry on top of them. Statins being the most obvious example.

What Lab Tests Are Actually Measuring

This is where the conversation takes a turn that will unsettle a lot of people, and Jordan is honest about that.

He introduces a concept called reification — the fallacy of treating an abstract concept as if it were a concrete reality. And his argument is that this is exactly what happens with most standard lab tests.

Take cholesterol again. Jordan walked through the history of how it was originally isolated — not found in the body as-is, but produced through a sequence of laboratory operations: boiling, heating, acidifying, using solvents, dehydrating, until a waxy powder remained. That substance was named, catalogued, and then a reagent was developed to detect something in human blood that reacts in a similar way to that powder.

What the test may actually be measuring, then, is a chemical reaction in your blood sample that resembles a chemical reaction caused by a man-made substance. Whether that reaction is specific to any particular molecule in your body — whether that molecule exists in your body in the way the model claims — is a separate question that largely hasn’t been answered.

Jordan’s analogy is memorable: the map is not the terrain. We can draw a map, give it a name, and use it to navigate. But the map is not the landscape. Reification is when we forget that distinction and start treating the map as the reality.

He applies the same logic to genes, amino acids, vitamins — and notes that across all of them, the pattern is the same. A lab process produces a substance, the substance gets a name, and then claims about what it does inside a living human body start to accumulate on top of that naming.

None of which means these measurements are entirely useless. Jordan isn’t claiming that. He uses lab work himself in practice. But he’s honest with his patients: your guess is as good as mine as to the exact meaning of this number. What he looks at instead is how people are actually feeling, how they’re sleeping, what they’re eating, whether they’re moving — the fundamentals that don’t require a reagent.

Normal Ranges and the Is-Ought Problem

David Hume, the eighteenth-century Scottish philosopher, observed something important: you cannot derive an ought from an is. Just because something is a certain way doesn’t mean it’s the way things should be.

Jordan brings this to blood test interpretation with real clarity.

When a lab prints a reference range on your results, what you’re looking at is a statistical norm — an average derived from a population sample. It tells you where most people in that sample fell. It does not tell you where you ought to be, and it doesn’t tell you what those numbers mean for your particular body.

He gives the example of cholesterol ranges in a population eating a standard Western diet versus a population eating primarily meat and animal fats. The ranges differ significantly. Which population provides the “correct” baseline? The question doesn’t really have an answer. But the ranges printed on your lab report imply one.

This is why Jordan spends time with his own patients explaining what reference ranges actually are before they start worrying about being a tenth of a point outside them. The anxiety generated by those red flags on a printout is, in many cases, arguably out of proportion to what the number actually tells us.

“Research Proves” and Other Phrases Worth Questioning

When someone says research proves or studies show, it’s worth pausing on what proof actually requires.

In the strict logical sense, proof means deductive reasoning — moving from a known premise to a necessary conclusion. What research studies typically offer is something much weaker: a correlation observed in a group of people, generalised into a claim about what will likely happen to a different person.

Jordan is careful not to dismiss studies entirely. He found them genuinely useful in surgical practice — particularly for showing that one procedure was no worse than another, or that one approach was statistically safer. That’s a reasonable use of the data. But it’s a long way from proof.

Jordan’s view is that statistics have in many cases become a way of maintaining the appearance of scientific rigour while doing something that strict rigour may not fully support. You cannot generalise from a particular to a universal — that’s the fallacy of induction — but that is exactly what most population studies invite you to do when applying their findings to your individual situation.

The counterfactual problem compounds this. You can never know what would have happened if you hadn’t taken the medication, hadn’t had the surgery, hadn’t followed the intervention. You might have recovered just as quickly. You might have done better. There’s no alternate universe available for comparison. And yet treatment decisions are routinely framed as if that comparison is settled.

Fear as a Clinical Tool

Several times in our conversation, Jordan returns to fear — the way it functions as a persuasion mechanism in medicine, often without the person using it even being aware that’s what they’re doing.

You need to do this, or your condition will get worse.

If you listen to people like him, you will die.

Both of those are real examples from the conversation. Jordan calls them appeals to the future and baseless assertions — making knowledge claims about what will happen without any genuine basis for that certainty. He also notes that they tend to appear when the evidence isn’t strong enough to carry the argument on its own.

His suggestion: if a provider is leaning heavily on fear to push you toward a decision, that’s useful information about the quality of the reasoning behind the recommendation. A genuinely well-supported recommendation doesn’t usually need this kind of pressure.

The reasoning Jordan describes isn’t presented as the whole picture of medicine, and he’s clear about that. Plenty of people working within conventional systems are doing their best with the frameworks available to them.

The respectful pushback question he recommends is simple: does this happen in a hundred percent of cases? If the answer is no — and it almost always is — then the certainty being implied isn’t warranted.

One Fallacy to Start With

If you were to choose one logical fallacy to learn to spot, Jordan’s answer is affirming the consequent. Not because it’s the only one worth knowing, but because it’s everywhere, and once you can identify it, you’ll start seeing it in almost every argument that appeals to an effect as proof of a cause.

After that, he’d add awareness of informal fallacies — ad hominem attacks, appeals to authority, and fear-based prognostication. Not because knowing the names matters, but because recognising the patterns helps you understand when an argument has stopped engaging with the evidence and started engaging with something else entirely.

The appeal to authority is worth a specific mention. Credentials are information about a person’s training. They are not evidence that any particular claim is true. Jordan is unusually direct about this: he says he would never appeal to his own credentials to support an argument he makes. The argument either stands on its own or it doesn’t. The letters after a name don’t change that.

What to Do With All of This

None of this is an argument for refusing medical care or treating every doctor as an adversary. Jordan is explicit about that. He still uses certain medications and interventions pragmatically, in the same spirit he’d wear glasses — not because the mechanism is fully understood, but because it helps.

What he’s arguing for is a different relationship with the claims being made — one grounded in honest questions rather than automatic compliance or automatic rejection.

Some practical approaches he suggests:

Ask your provider to explain things in plain language, not jargon. If the explanation only makes sense in terms of invisible mechanisms, it’s worth probing further.

Don’t make a decision in a ten-minute appointment under pressure. You can take information away, think about it, and come back.

Ask whether there are alternatives, including doing nothing. There usually are, and a provider who presents only a binary choice may not be giving you the full picture.

Know your own principles in advance — what kinds of interventions you would or wouldn’t want — so that fear in the moment doesn’t override thinking you’d want to do when you’re calm.

And underneath all of it: ask how do you know that? Not aggressively, not as a gotcha, but as a genuine question. A provider who can’t answer it, or who responds with dismissal or ridicule, is telling you something important.

A Final Thought

It is worth noting that the system cannot withstand sustained lines of honest inquiry. Not because it’s dishonest at every level — plenty of people working within it are doing their best — but because the foundations of some of its most fundamental claims are shakier than the confidence with which they’re delivered.

The way through, in Jordan’s view,  isn’t hostility. It’s curiosity. It’s the same impulse that had a young Jordan Grant asking his Sunday school teacher about the nature of eternity, but how do you actually know that?

That question is worth asking. It’s always worth asking. And if the answer you get is fear, a personal attack, or a rehearsed non-answer, it’s may be worth seeking a second opinion.