When is something normal, and when is it abnormal?
How can we assess whether a human trait is a “normal variant” or something abnormal?
Just because something is common doesn’t necessarily make it normal - or desirable. It’s a fallacy to use population averages as a measure of health. Especially in modern societies, where lifestyle and environment often differ radically from the evolutionary conditions we are biologically adapted to.
At the same time, frequency isn’t irrelevant. Biological variation is the product of evolution, and traits that occur frequently may reflect evolutionary adaptations. That doesn’t mean they're optimal in a modern context – but they’re not automatically pathological either.
So, how can we meaningfully distinguish normal from abnormal? A more nuanced approach can be based on a combination of four criteria:
Statistical distribution
Is the trait unusual in the population? This can provide a first indication, but it says nothing about harm.
“Normal” is often defined as falling within ±2 standard deviations of the mean (≈95% of the population). But a condition can be common (e.g., overweight) and still be physiologically disadvantageous or associated with poor health outcomes.
Functional impact
Does the trait impair the body’s ability to maintain homeostasis and function effectively? If so, it’s typically considered abnormal.
But function isn’t always clear-cut. For example, elevated BMI is a risk factor in younger adults, but in older populations, the pattern can reverse (the “obesity paradox”).
Prognosis
Prognosis is the clinical anchor and often the main reason to intervene.
Does the trait increase the risk of disease, impaired function, or premature death? If a physiological state predicts negative outcomes, it can reasonably be considered abnormal, even if it’s common.
Still, context matters. Not everyone with a mildly abnormal marker (e.g., low vitamin D or borderline-high blood pressure) should be labeled ill. Overdiagnosis is a real risk if every deviation from “normal” is pathologized.
Context
Age, sex, physical fitness, and biological adaptation all matter greatly. A trait can be normal in one context and abnormal in another.
A classic example is a resting heart rate of 45 bpm: in an untrained individual, it could indicate sinus bradycardia and carry risk, while in an athlete, it reflects normal adaptation.
Another is low estrogen: normal in menopause, but abnormal in young women.
There are many examples of biological adaptations shaped by evolution that are beneficial in specific environments.
Insulin resistance during pregnancy is physiologically adaptive, whereas the same phenomenon outside pregnancy is pathological. Similarly, higher hemoglobin levels are adaptive at high altitudes but may be abnormal at sea level.
One final point
Just because something is “evolutionarily natural” doesn’t mean it’s beneficial or desirable in today’s context.
Evolution optimizes for reproductive success, not long-term health.
So ...
A trait isn’t automatically normal just because it’s common, and not automatically abnormal because it’s rare. It depends on how it functions, what it means for health, and the context in which it appears.
We must consider statistical distribution, physiological function, prognostic value, and context. And at the same time, be mindful of the danger of pathologizing normal variation or ignoring the clinical relevance of biological frequency.
Ultimately, assessment must be individualized, evidence-based, and context-sensitive. Yet there’s always a tension between nuanced individual assessment and the need for pragmatic, generalizable thresholds in decision-making.
And that’s without mentioning traits that aren’t easily measurable, but depend on cultural, social, or moral norms.”
So, in that light, is neurodiversity abnormal, or a normal variant?
Phew, it ain’t easy.
About the paper that inspired:
Published: The Economist, October 2024
Link to paper: https://www.economist.com/science-and-technology/2024/10/30/researchers-are-questioning-if-adhd-should-be-seen-as-a-disorder
Comments ()