A Selection Paradox
Why All the Hot People
You Date Are Assholes
It's not bad luck. It's not your taste. It's selection geometry.
Here's a pattern everyone recognizes: the attractive people you date tend to have terrible personalities. And the really nice ones? Somehow less attractive.
Most people chalk this up to life's unfairness, or mumble something about trade-offs. Hot people don't need to develop personalities. Nice people compensate for their looks.
But there's a deeper explanation. One that has nothing to do with actual trade-offs between traits. It's called Berkson's Paradox, and once you see it, you'll find it everywhere.
The correlation is real. But it's created by your selection process, not by nature.
The Dating Paradox
Imagine the dating pool as a 2D space. The X-axis is attractiveness. The Y-axis is personality. Each dot is a person.
In the general population, these traits are uncorrelated. Hot people and ugly people have the same distribution of personalities. The correlation is essentially zero.
The pink dashed line is your dating threshold.
You'd date anyone above the line (combined score > 100).
Now watch what happens. As you raise your standards (slide the threshold up), the correlation among people you'd actually date becomes increasingly negative.
This isn't because attractive people have worse personalities. It's pure selection geometry.
The mechanism: Among people who pass your filter, someone with exceptional looks only needed a mediocre personality to make the cut. Someone with exceptional personality only needed mediocre looks. The filter creates the trade-off.
The Collider Structure
In causal inference, this is called conditioning on a collider. Here's the structure:
A and B are independent. No correlation between attractiveness and personality.
Both attractiveness (A) and personality (B) influence whether someone makes it into your dating pool (C). C is the collider.
When you condition on C (look only at people you'd date), you create a spurious association between A and B. The causal graph literally grows a new edge by selection.
This is Berkson's Paradox: conditioning on a common effect creates correlation between independent causes.
Real World Examples
The Hospital Paradox
In the general population, diabetes and broken legs are unrelated. But among hospitalized patients, they appear negatively correlated.
Patients are hospitalized if either condition is severe enough.
Among the hospitalized, the conditions appear negatively correlated.
Why this matters: Early epidemiological studies made exactly this mistake. They found spurious correlations between unrelated diseases because they only studied hospitalized patients. Joseph Berkson identified this in 1946.
The Startup Paradox
Why do successful startups seem to have either amazing tech OR amazing marketing, but rarely both? In the full population of startups, there's no trade-off. But you only see the successful ones.
Startups succeed with high combined score (tech + marketing).
Among survivors, the qualities appear to trade off.
Survivorship bias + Berkson's: You only observe successful companies. Among those, great tech substitutes for great marketing and vice versa. This creates the illusion of a trade-off that doesn't exist in the underlying population.
Hollywood Actors
Why do movie stars seem dumb? To become famous, you need either exceptional talent OR exceptional looks. Among the famous, talent and looks appear negatively correlated.
Academic Papers
Published papers with surprising results tend to have weaker methodology. Strong methodology + surprising results would definitely get published. Weak methodology only gets through if the results are exciting enough to overlook flaws.
Restaurants on Yelp
Why do cheap restaurants often have better food than expensive ones? You only visit restaurants that are worth it. A cheap place needs great food. An expensive place can coast on ambiance.
How to Avoid the Trap
Once you understand Berkson's Paradox, you can catch yourself making this mistake:
1. Ask: What selected this sample?
Before concluding two things are related, ask what process determined which observations you're seeing. Hospital admission, publication, success, your attention - all are selection filters.
2. Draw the causal graph
If two variables both point to a third (the collider), and you're conditioning on that collider, expect spurious correlation. The math is predictable.
3. Look at the unfiltered population
When possible, check if the correlation exists in the general population, not just your selected subset. If it disappears, you've found Berkson's Paradox.
4. Remember: Selection creates structure
Your observations are not a random sample of reality. They're filtered through processes that can create patterns that don't exist in nature.
So about those hot assholes you keep dating...
It's not them. It's not you. It's geometry.
The moment you filter for "worth dating," you create a world where attractiveness and personality trade off. The paradox is built into the selection process itself.
Berkson's Paradox: a reminder that what we observe is always shaped by how we came to observe it.
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Reference: Berkson (1946)