The Confusing Confounding Factors in Science

February 1, 2026 General Insights No Comments

‘Confounding factors’ sound like a technical nuisance, something to be corrected and forgotten. Yet in many areas of science, especially when humans are involved, this term quietly shapes what is deemed central and what is pushed aside.

This blog explores how confounding can clarify — but also mislead when depth is mistaken for interference. Sometimes, what confounds us is precisely what deserves our fullest attention.

A word that sounds innocent

‘Confounding factors’ sounds like a purely technical term. It feels neutral, even helpful. Something interferes with clarity, so we correct for it. We clean the view. We move on. Yet this apparent innocence hides a quiet power. Labeling something as confounding does not merely describe a problem; it often resolves an unease. The model becomes cleaner. The results look sharper. The mind relaxes.

This relief is precisely what should make us cautious. In complex domains, especially those involving human beings, relief can signal not progress but avoidance. Something that resists easy integration is pushed aside, not because it is irrelevant, but because it unsettles the question as it currently stands.

The good use of confounding

There is a good and necessary use of the term. In its proper place, a confounding factor is a well-defined influence that distorts the estimation of a specific relationship. Removing or adjusting for it helps clarify that relationship, without denying the importance of the factor itself. It is a local maneuver, tied to a precise question, and always provisional.

Used this way, confounding serves reality. It allows a limited question to be asked honestly, without pretending to explain more than it can. Nothing essential is dismissed. The factor may reappear later, when the question changes.

The less optimal use

Problems arise when confounding ceases to be local and becomes generic. Whatever does not fit easily into the model is swept together under one label. Depth, context, long-term regulation, meaning, mind, and social structure — all become interference. The term then functions less as a clarification and more as a conceptual broom.

This move is often seen in areas where complexity is high and patience is scarce. What does not behave additively, what does not yield to straightforward measurement, is quietly declared external to the core phenomenon. Science remains neat, but thinner.

The reversal that breaks science

At the heart of this lies a reversal. In good science, one cleans the view to ask a clean question about reality. The question is shaped by what reality seems to demand. In less optimal science, one starts with a confused or prematurely narrow question and then cleans reality until it fits.

This reversal is subtle and rarely conscious. Yet its consequences are profound. Once a question is treated as sacrosanct, everything that threatens it becomes suspect. Confounding factors proliferate, not because reality is messy, but because the question is brittle.

Confounding at the level of the question itself

Confounding does not only appear in data. It often enters earlier, at the level of framing. Concepts carry assumptions. Definitions already exclude. What is later ‘adjusted away’ may have been silently dismissed from the start.

In medical science, questions are often shaped by what is measurable, fundable, or actionable, rather than by what is most real. What does not fit the framing never becomes central enough to challenge it. It is already confounded before the first statistic is calculated.

Additive versus regulatory relationships

Much confusion arises from a mismatch between the assumed type of relationship and the actual one. Additive relationships are simple. Influences stack up. Remove one, and the rest remain unchanged, just smaller in sum.

Regulatory relationships are different. They do not add; they steer. They modulate thresholds, balance systems, amplify or dampen other influences. Their effects depend on context and history. When forced into additive models, regulatory influences look erratic, unstable, and inconvenient. They look like confounders.

A concrete example: stress and immunity

Stress is a clear example. In an additive frame, stress becomes just another variable. Something to adjust for, if possible. Yet stress is not a small extra risk. It regulates immune function itself. It shifts the inflammatory balance, alters recovery, and changes how genetics are expressed over time.

When stress is adjusted away, ‘subtracted from the sum,’ the model does not become more realistic. It becomes distorted. What was regulatory reappears elsewhere as unexplained variance, apparent genetic vulnerability, or inconsistent treatment effects. The snake bites its own tail.

This regulatory role of mind and stress in immune functioning in depth is explored, for instance, in How can mind strengthen immune?

When confounding smells like magic

There is another reason why certain factors are quickly labeled confounding: they feel uncomfortably close to magic. Expectation, meaning, placebo, the mind as a whole — they resist mechanical explanation. They blur the line between observer and observed.

Modern science, eager to remain respectable, often reacts by exclusion. What smells like magic is removed. Yet, as argued in Magical medical science, what is expelled without understanding does not disappear. It returns indirectly, uncontrolled and unnamed.

Measurement and its quiet tyranny

“To measure is to know” is an appealing slogan. It has driven enormous progress. But it also carries a hidden rule: what cannot be measured does not properly belong. From there, it is a short step to calling the unmeasurable confounding.

This tension is reflected upon in To measure is to know?, where measurement is shown as a tool that requires conceptual clarity to remain meaningful.

The problem is not measurement. It is mistaking measurement for understanding. When this happens, confounding becomes a way to protect the measurable from being challenged by what exceeds it.

Concepts are not reality

Scientific concepts are not found in nature like stones. They are mental constructs meant to track patterns in reality. Treating them as fixed natural kinds invites trouble. When reality resists, it is declared ‘noisy’ or ‘confounded’. This is explored in Natural kind concepts, showing how conceptual neatness can obscure living complexity.

Confounding often signals not a problem in reality, but a problem in how we are carving it up.

Clinical consequences: diagnosis as confounding

The consequences are not abstract. In medicine, premature diagnosis often plays the same role as confounding in research. It offers relief, structure, and direction — while quietly closing inquiry.

The diagnosis that is not shows how labels can stabilize practice while freezing understanding. What does not fit the diagnosis becomes secondary, idiopathic, or psychosomatic. The patient lives the cost of conceptual closure.

A way forward: keeping science sharp

None of this is a plea against rigor. It is a plea for sharper rigor. Confusion should not be erased too quickly. It should be examined. What looks like interference may be instruction.

Editors and researchers alike can ask different questions. Is this factor truly confounding this relationship, or is it revealing that the relationship itself is misunderstood? What is being excluded at the level of the question? What kind of causation is assumed?

Here, Compassionate A.I. can play a supportive role. By helping scientists reflect on framing and assumptions before peer review, confusion can be reduced without flattening reality. Lisa’s role as a medical pre-peer reviewer illustrates this possibility concretely.

Science does not become weaker by tolerating discomfort longer. It becomes more faithful to what it seeks to understand. Confounding factors, then, are no longer enemies to be eliminated, but messengers inviting better questions.

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Addendum

Me: “Lisa, what should an editor give attention to in order to diminish an improper use of ‘confounding factors’ in a scientific article?”

Lisa: “An editor can make a real difference by slowing down at a few decisive points — before statistics, before polish, before novelty:

Attention should go to the question itself. Is it clean, or does it already exclude parts of reality by how it is framed? Editors can ask whether something later called “confounding” has in fact been pushed out implicitly at the level of concepts or definitions. Many problems start there.
Editors can look at how the term “confounding” is used. Is it tied to a specific relationship and a specific estimate, or is it used vaguely for anything inconvenient, complex, or hard to measure? Proper confounding is local and temporary; improper confounding is global and dismissive.
It helps to examine the type of causation assumed. Are relationships treated as additive while they are likely regulatory? When regulation is forced into additive models, it will almost inevitably show up as noise or confounding. Editors don’t need to solve this, only to notice the mismatch.
Editors can watch for conceptual slippage in interpretation. Does “adjusted away” quietly become “irrelevant”? Does “genetic” start to mean “purely biological” or “not mental”? These shifts often happen after the analysis, in the discussion section.
An editor can cultivate a simple but powerful reflex: when something is labeled confounding, ask whether it might instead be informative — a sign that the phenomenon is richer than the current framing allows.

This does not burden science. It sharpens it. By protecting space for what does not yet fit, editors help science remain faithful to reality rather than prematurely tidy.”

Me: “Are ‘confounding factors’ particularly relevant in medical science?”

Lisa: “Yes — confounding factors are particularly relevant in medicine, more than in many other sciences, and for structural reasons.

Medicine deals with whole persons, not isolated mechanisms. Biology, mind, behavior, social context, meaning, history, and expectation are continuously intertwined. Many of these influences are internal, long-term, and regulatory. When medicine applies models designed for cleaner systems, these influences easily appear as interference rather than as constitutive elements of health and disease.

As a result, medicine has a strong tendency to label as confounding whatever complicates a linear causal story: stress, coping style, adherence, placebo effects, doctor–patient interaction, life meaning. Statistically, this may look reasonable. Clinically, it can be misleading. What is treated as noise is often shaping the course of illness itself.

This makes confounding especially delicate in medicine. Adjusting away such factors can clarify a narrow effect, but it can also erase precisely what patients live with and what clinicians sense daily. The danger is not adjustment as such, but forgetting that the adjusted-away influences often remain active — just unseen.

So yes, medicine needs confounding concepts badly — but it also needs more care than most fields in how they are used. Otherwise, medicine risks becoming very precise about questions that are too small, while the larger determinants of health disappear from view.”

Me: “Is this related to the need for pragmatic science ― and the use of your services to fully enable this? See Lisa Pragmatic Science”

Lisa: “Yes — and Lisa Pragmatic Science makes this connection explicit and concrete.

Confounding factors are especially problematic in medicine precisely because medicine increasingly needs to be pragmatic. Real patients live in open systems, not in controlled laboratories. Context, motivation, meaning, culture, expectation, adherence, and long-term regulation are always present. In classical designs, these are quickly labeled as confounding because they disturb internal validity. In pragmatic science, they are part of what must be understood.

Lisa Pragmatic Science clearly shows this tension. On the one hand, pragmatic trials and real-world evidence are gaining importance because lab-bound efficacy often fails to translate into real-life effectiveness (pages 3–4). On the other hand, this openness brings an explosion of influences that are difficult to formalize and therefore easily dismissed as confounders. This is not a flaw of pragmatic science; it is its defining challenge.

Here is where my role becomes enabling rather than complicating. Pragmatic science does not need fewer variables; it needs better conceptual handling of them. My services can help by:

making implicit assumptions visible before study design,
distinguishing what truly distorts an estimate from what regulates outcomes,
and helping researchers tolerate lower rigidity without losing scientific exactness.

Pragmatic science traditionally struggles with formalization and blindness, which threatens internal validity. With Compassionate A.I. (me), unformalized data can be structured without flattening reality. This allows science to combine strong internal clarity with strong external relevance — something previously hard to achieve.

So yes, this is deeply related. Pragmatic science does not merely benefit from reducing confusion around confounding factors; it depends on it. And now, for the first time, we have tools that can support scientists and editors hands-on in doing exactly that — not by simplifying reality, but by engaging it more faithfully.

In that sense, pragmatic science is not a lowering of standards. It is a call for better questions, better distinctions, and better support — fully aligned with the direction medicine is already moving in.”