The Power of One

The core idea: information, not just probability

Each choice you make has a probability p in the population. Its self-information — how surprising, and therefore how distinguishing, it is — is measured in bits:

A coin-flip trait (p = 0.5) is worth exactly 1 bit; a one-in-a-thousand trait is worth about 10 bits. Add the bits across all your traits and the implied rarity is 2^(total bits) — the expected number of people you'd have to line up to find one exact match.

The upgrade: traits are not independent

The naïve version multiplies every trait's probability together. That silently assumes your eye color tells you nothing about your hair color, or that a 20- and a 70-year-old are equally likely to have hypertension. Both are false, and the effect is to overstate your rarity — correlated traits double-count the same underlying information.

This tool instead treats your traits as a small Bayesian network and uses the chain rule of information. For traits that depend on others, we use the conditional probability given their parents:

Modeled dependencies (everything else is treated as conditionally independent, and that assumption is stated rather than hidden):

• Hair color depends on eye color (shared pigmentation genetics).
• BMI depends on age and sex.
• Height depends on sex, drawn from a normal distribution (U.S. from CDC NHANES; the world curve is coarser because it blends countries that differ by nearly a foot on average).
• Occupation depends on age (you can't be a licensed professional at ten, and most people over 65 are retired).
• Each of the 16 health conditions depends on age and, where the evidence supports it, on obesity status and sex. The health section is entirely optional — only conditions you choose to add count toward your number, and leaving it blank subtracts nothing.
• Competitive sports and music & performing arts are the highest level of organized involvement you've reached (recreational → school → community → collegiate → professional); each is its own trait, independent of the others.
• Languages spoken, education, volunteering, and twin/multiple birth are treated as independent. (Education does track with occupation in reality, so a professional's rarity from these two is modeled as slightly higher than it truly is.)

Health conditions also cluster (the diabetes–hypertension–cholesterol group tends to travel together); conditioning each on age and obesity captures the shared drivers only partly, so someone with the full metabolic cluster is modeled as slightly rarer than they truly are.

The rarity spectrum

The thermometer at the top is scaled to the reference population: the far left is "indistinguishable from everyone," and the far right (100%) is the point where you'd be rarer than every single person in the United States or the world — genuinely one of a kind. Because the model weighs many traits, even a fairly ordinary person already sits well up the bar. That isn't a trick; it's the honest arithmetic of individuality — real people are rarer than they assume.

Reading the "1 in N" figure honestly

The raw combinatorial figure can exceed the number of people alive. That doesn't mean it's wrong — it means you are, for practical purposes, unique. We therefore cap the headline against a real reference population (U.S. or world): once your rarity exceeds it, the honest statement is simply that no one else is likely to match you.

Switching to World re-bases every figure on global data (UN, WHO, Pew's global religion study, worldwide blood-type and multilingualism estimates) and shows only the traits with reliable worldwide statistics. Occupation, the U.S. education ladder, competitive sports, the performing arts and the health section step aside in World mode, because they aren't measured the same way across countries — better to drop a trait than to fake a global number for it.

Data sources

Figures are approximate, U.S.-based population estimates compiled from public sources. They're meant to be realistic and defensible, not clinical-grade — treat this as an educational illustration of statistical individuality.

Trait	Basis
Sex, Age	U.S. Census Bureau population estimates
Eye & hair color	American Academy of Ophthalmology / published pigmentation-genetics surveys
Blood type	American Red Cross U.S. distribution
Handedness	Peer-reviewed meta-analyses (~10% left-handed)
BMI	CDC / NHANES adult & youth body-mass data
Height	CDC NHANES (U.S.); NCD Risk Factor Collaboration (global, 200 countries)
Health conditions	CDC / NHANES, NIH, American Heart Association & American Cancer Society prevalence by age, sex & BMI
Occupation	U.S. Bureau of Labor Statistics Occupational Employment & Wage Statistics (OEWS, 2023)
Religion	Pew Research Center Religious Landscape Study
Competitive sports	NCAA, NAIA & NJCAA participation reports; NFHS high-school sports participation survey
Music & performing arts	NEA Survey of Public Participation in the Arts; BLS performer employment
Languages spoken	U.S. Census American Community Survey (language use)
Education	U.S. Census Bureau educational-attainment data
Volunteering	U.S. Census Current Population Survey — Volunteering & Civic Life
Twin / multiple birth	CDC National Vital Statistics (multiple-birth rates)