How We Calculate Your Rarity Score
A plain-English explanation of the math behind your score, its limitations, and why the number is more meaningful than it might first appear. And less precise than it looks.
The basic idea
For each trait you select (eye color, blood type, MBTI type, income bracket, and so on) we look up the estimated global prevalence of that trait. For example, roughly 9% of people have blue eyes, 3% of people are ENTP, and about 3% of people earn over $100K/year.
We then multiply these probabilities together to produce a combined score representing the estimated rarity of your specific combination of traits.
= 0.09 × 0.032 × 0.10 × ...
= 1 in [very large number]
The honest limitation: independence
This approach assumes each trait is statistically independent: knowing your eye color tells us nothing about your MBTI type, income, or whether you can roll your tongue. For many trait pairs, that's roughly true.
But not all. Hair color and eye color are correlated. MBTI introversion and chronotype have some overlap. Income and education are linked. When correlated traits are multiplied together, the combined number gets inflated beyond what's technically accurate.
Think of it like this: if you shuffled 8 billion people and tried to find someone who matched you on all 35 traits simultaneously, the probability of a match on any given person is what the score represents. Even if the pool of people is not large enough for that probability to resolve to a whole person.
Why we kept it this way
A fully correlated Bayesian model accounting for every known trait relationship would require data that doesn't exist publicly, would vary significantly by geography and ancestry, and would still be an approximation.
The independence model still captures something real: combinatorial uniqueness. Even if two traits are correlated, the odds of matching someone on both simultaneously is still lower than matching on one. Stack 35 traits and the number explodes. not because we're lying, but because identity genuinely has that many dimensions.
Where the probabilities come from
Each trait's prevalence is sourced from published research and global datasets. We use global averages where available, which means the numbers are population-weighted across all countries rather than US-specific.
Blood type: ISBT global registry
Handedness: McManus et al. 2023
Height: NCD-RisC / Lancet 2020
Vision: WHO Global Eye Health Report
Cilantro aversion: Eriksson et al. 2012
Widow's peak: Nusbaum & Fuentefria 2009
Earlobes: Bhanu & Malhotra 1972
Lactose: Itan et al. 2010
Introversion: Cain, Quiet (2012)
Chronotype: Roenneberg et al. 2007
HSP: Aron & Aron 1997
International travel: UNWTO 2023
Natural disasters: EMDAT database
Tattoos: Ipsos / Harris Poll 2023
Income: World Inequality Database
Work situation: ILO World Employment
Remote work: McKinsey Global Institute 2023
A note on MBTI
MBTI has well-documented criticism as a psychological instrument. Its test-retest reliability is imperfect and the underlying theory is contested. We include it because it's widely known and self-reported, not because we're endorsing it as rigorous science. If you don't know or don't trust your type, the personality category is still valid through the other five questions.