Guides

NewbornNames has one data source and one build pipeline. The source is the U.S. Social Security Administration's national baby-names dataset, published as public-domain government data from Social Security card applications. The pipeline is a committed script that selects subsets of that archive and attaches ranks, without editing a single name or count. These 8 guides describe both in enough detail to check our work. Every figure a guide quotes is computed at build time from the same files the generator and the 52 letter pages read: the 2025 top 1,000 per sex, the 2025 top 50 per starting letter per sex, and the top 200 per sex for the 7 snapshot years 1955, 1965, 1975, 1985, 1995, 2005, 2015.

What the guides cover

Four questions run through all of them. First, where the numbers come from: the SSA's archive, its own qualifications, and the retrieval that fixed 2025 as this site's headline year. Second, what the numbers mean: a rank is a position within one sex for one year by descending count, and a count is the number of card applications carrying that exact spelling, so in 2025 the boys' list runs from Liam at 20,818 births to #1,000 Langston at 227. Third, where the data stops: names given fewer than 5 times in a year are excluded by the SSA, the committed depths are fixed, and the lists that stop short are Q for girls at 34, U for girls at 32, Q for boys at 39; nothing is padded past any of those edges. Fourth, what the site will not say: no meanings, origins, or etymologies, no "unique" or "rare" verdicts beyond a stated rank and count, and no names the SSA never recorded.

How SSA baby-name data works

The source guide. It walks through the SSA archive, one file per birth year with lines of name, sex, and count, and the script that turns it into the three committed files behind this site. It restates the SSA's nine qualifications in plain terms: card applications rather than birth certificates, the fewer-than-5 exclusion, coverage limited to the 50 states and D.C., separate counting of spellings, the record restrictions, hyphen and space removal, unedited entries, alphabetical tie-breaks, and per-sex rankings.

Reading ranks and counts

What a rank is, what a count is, and how steeply counts fall as ranks descend. The guide works the 2025 top 1,000 for both sexes; among girls, #1 Olivia at 13,544, #100 Juniper at 2,618, and #1,000 Harmoni at 252 show the fall-off rather than describe it. It also covers two conventions that confuse first-time readers: alphabetical tie-breaks, so adjacent ranks can carry identical counts, and per-sex rankings, so one spelling can hold two ranks in the same year.

Spelling variants chart separately

The SSA counts each spelling as its own entry, and strips hyphens and spaces before counting. This guide shows what that does to the tables, using clusters of related spellings found in the 2025 data with each spelling's own rank and count. It does not merge them: a combined "true" popularity would be a number the SSA never published, so the site does not compute one.

Decade snapshots, explained

Every decade era on this site is a single year of SSA data, not an average. The guide names the 7 snapshot years, explains why each snapshot carries only the top 200 per sex, and lists the #1 name in each. In the oldest snapshot, 1955, those were Mary at 63,174 girls and Michael at 88,355 boys. It also covers the generator's 8 era options and why tiers past rank 200 are structurally empty for a decade era.

First-letter trends since 1955

For each snapshot year and each sex, the share of top-200 births taken by every starting letter, as full A–Z tables. The movement is stated as data, not cause: among girls, the initial with the largest share was D at 13.8% in 1955 and A at 20.2% in 2015, and in the 2025 top 1,000 it is A at 16.7%. The boys' tables sit alongside the girls'.

Short, medium, and long names

The generator's three length classes, counted on the SSA spelling: short is four letters or fewer, medium is five to seven, long is eight or more. The guide gives the distribution in the 2025 top 1,000, where girls' names split 146 short, 723 medium, and 131 long, and boys' 183, 739, and 78, then repeats the count for each snapshot year.

How the name generator samples

How a spin works, precisely enough to be checked. The generator filters the chosen era's list by letter, length class, and popularity tier, then draws from the browser's cryptographic random bytes with rejection sampling, so every matching name is exactly equally likely. Draws of more than one name, up to 20, use a partial Fisher–Yates shuffle so no name repeats. The guide also explains why an empty result is a real result: the generator says whether a tier lies beyond the era's depth or simply nothing matched, and never pads the list.

What this site does not publish, and why

The provenance stance in full. The site has no provenance-clean source for meanings, origins, or etymologies, so it publishes none rather than fabricating or scraping them. It attaches no "uniqueness" score beyond what a rank and count state, with the fewer-than-5 caveat attached. It generates no names: every name it shows was given to at least 5 U.S. babies of that sex in the displayed year. The guide explains each refusal and the provenance rule behind it.

Where to go next

The guides describe the data; the tools show it. The name generator draws from the 2025 list or any of the snapshot years. The top 100 boy names and top 100 girl names pages carry the 2025 ranks and counts with an A–Z entrance to every letter page. The methodology page is the compact reference these guides expand on, and the about page says who publishes the site and why it keeps to one source.

Source: every name, rank, and count on this page comes from the U.S. Social Security Administration's national baby-names dataset (1955–2025 data, from Social Security card applications). It is public-domain U.S. government data; names given fewer than 5 times in a year are excluded by the SSA, spelling variants are counted separately, and the data covers U.S. births only. Full caveats on the methodology page.