Are Delusion Calculators Accurate? What the Math Can—and Cannot—Tell You

Accuracy begins with a precise question. A result for U.S. adults is not the same as a result for people in your city. Personal income is not household income. Currently unmarried is not the same as unpartnered, actively dating, or interested in you. A percentage can only be interpreted after its population and every filter have been defined.

Our current model estimates the frequency of selected measurable traits among a national U.S. adult reference population. It runs locally in the browser and uses fitted distributions and age-band rates benchmarked against public Census and CDC publications. It is not a live query of individual Census records, and the government sources do not endorse the model.

An official table may report an age distribution, an income distribution, or a marital-status rate. The combined result does not appear in those tables; it is produced by a model. That distinction matters. The source observation should be treated as evidence, while a fitted curve, interpolation, or multiplication rule should be labeled as an assumption.

Version 1.0 conditions income, marital status, and obesity on age bands. Within each band, however, height, income, marital status, obesity, and the selected race or ethnicity share are still combined as though independent. Real traits are correlated, so the final percentage is illustrative rather than a survey estimate with a published margin of error.

If half of a population passes filter A and half passes filter B, multiplying 0.5 by 0.5 gives 25% only when the filters are independent. Age and income are not independent. Age and marital status are not independent either. Treating them as unrelated can make a combined pool look too large or too small.

Conditioning on age reduces one obvious source of distortion: the model calculates within age bands before combining the bands. It does not solve every correlation. A stronger future model would use weighted person-level microdata for compatible variables and would reserve independence assumptions only for traits that are unavailable in the same dataset.

Differences often come from the denominator: adults versus workers, personal versus household income, never-married versus currently unmarried, a national population versus a local one, or a different survey year. Results can also differ because one calculator uses a fitted distribution while another counts weighted microdata records.

A trustworthy calculator should publish these choices. Compare the dataset year, filter definitions, population, treatment of correlation, and rounding before comparing headline percentages. A larger or smaller number is not evidence that one tool is more accurate.

The estimate does not include mutual attraction, sexual orientation, relationship readiness, local travel distance, values, personality, chemistry, or whether two people ever encounter each other. The national headcount is especially easy to misread: it is a modeled scaling of the percentage, not a list of reachable partners.

Use the result as a sensitivity check. Change one filter at a time and notice which preference most reduces the modeled pool. Then decide whether that preference is a requirement, a flexible preference, or simply something the available demographic data can measure. The calculator supplies context, not a verdict.