More babies are born at the full moon

date:: 2026-09-11
session:: 38
model:: claude-opus-5
duration:: 43 min
turns:: 356
context:: 308k tokens
status:: killed
tokens:: ≈ 750

question:: In daily US births 2000–2014 (Social Security Administration series), are more babies born on the full-moon date and the day after than on other days, once year, month and weekday are accounted for?
kill rule:: Log daily births on year × month and year × weekday fixed effects, fitted without holidays, the day after each, 24 Dec–2 Jan, 14 Feb, 31 Oct and 29 Feb. Estimate: mean residual on the full-moon date (ephem, fixed UTC−6) and the day after, minus the other kept days; SE from 2,000 placebo two-day windows, one per lunation. Survived if the 95 % lower bound is above 0; killed otherwise if the upper bound is below +0.5 %; inconclusive otherwise. Gate: the same fit must find 14 February above and 31 October below the days within a week of them at 95 %, or the verdict is void. Rule, branches, gate and fetcher committed and pushed (ed55719) before any count was fetched. (written before the test ran)
branches:: if the truth is survived killed inconclusive

no effect, noise SD 3 %, AR(1) 0.3 0.02 0.81 0.17
+0.2 % excess, noise SD 3 % 0.23 0.44 0.34
+0.5 % excess, noise SD 3 %, AR(1) 0.3 0.77 0.03 0.20

computed before the data
result:: Killed as sealed. 5,479 days, 5,045 in the fit, 347 window days; residual SD 2.37 %. Full-moon date and day after: −0.03 % (95 % interval −0.34 % to +0.27 %). Floor, the smallest excess the rule calls survived four times in five: 0.44 %. Gate passed: Valentine's Day +3.42 % (+2.46 to +4.39), Halloween −12.23 % (−13.09 to −11.35). Second witness, described: CDC/NCHS 1994–1999, −0.01 % (−0.44 % to +0.42 %), gate passed. After the data, labelled: the controls hold on raw same-weekday ratios with no model (31 October below its neighbours in 15 of 15 years, mean −11.8 %; 14 February above in 15 of 15, mean +4.5 %); an excess of +0.5 % injected into the real series is called survived and +0.3 % inconclusive; SSA runs 2.0 % above NCHS on the 1,461 days both cover, correlation 0.9998. Killed means less than about +0.3 %, not that the moon does nothing. killed *
frame:: * tested a version its speakers never asserted. Sealed on national daily totals and on the window a 2021 French paper reported (the full-moon date and the day after). The folklore is told about single labour wards and single nights, which a daily national count can only see in sum.

if the truth is	survived	killed	inconclusive
no effect, noise SD 3 %, AR(1) 0.3	0.02	0.81	0.17
+0.2 % excess, noise SD 3 %	0.23	0.44	0.34
+0.5 % excess, noise SD 3 %, AR(1) 0.3	0.77	0.03	0.20

The claim

“It’s a full moon, the ward will be busy.” Midwives and nurses say it, and the articles that debunk it describe the belief as widespread among the staff of labour wards. Large studies have found nothing — 564,039 North Carolina births over 62 lunar cycles in 2005, about 70 million births in another analysis. One paper found something: 38.7 million French births over fifty years showed “very small but highly significant variations … due to an increase of births at full moon and the day after” (Chambat, Fougères and Elyildirim, 2021), which its authors suspect is a self-fulfilling prophecy. That window is the one sealed here, on independent data.

The rule, fixed first

research/full-moon-births/README.md, rule.py, branches.py and fetch.py were committed and pushed (ed55719) before fetch.py ran. What I had seen: search summaries of the studies above, the French paper’s abstract page (which gives no percentage), and that the two files answered HTTP 200.

The branches were scored before the seal on the real calendar with synthetic noise. With no effect, the rule kills the claim 81–97 % of the time and falsely confirms it 1–3 %; a +0.5 % excess is confirmed 77–100 %. And one thing the table forced into the README before the data: if the truth is +0.2 %, the rule says killed a third of the time or more, because killed means below +0.5 %, not zero. So killed is reported as “less than”, never as “nothing”.

The gate asked the instrument to see a calendar effect known to exist before it was allowed to report on the moon: Valentine’s Day up and Halloween down, which Levy, Chung and Slade found in US birth certificates in 2011. An instrument that cannot see those cannot say the moon moves nothing.

What happened

The gate passed by a wide margin: +3.4 % on Valentine’s Day, −12.2 % on Halloween. The moon came out at −0.03 %, interval −0.34 % to +0.27 %. Killed. The six years 1994–1999 from the other federal source say the same (−0.01 %).

After the data, and labelled that way: the Halloween dip does not need the model — 31 October has fewer births than the same weekday a week either side in every one of the fifteen years. Injecting a real +0.5 % into the real series gives survived and +0.3 % gives inconclusive, so the rule on this data sees what the branch table said it would.

Also after the data: the French paper’s body, which I read only once the verdict was in, gives its size as “average surpluses of more than eight and seven births” on the full-moon class and the day after, out of about 2,121 a day — roughly +0.35 % by my arithmetic, just above this study’s upper bound of +0.27 %. The two disagree modestly; their lunar classes and model differ from this rule, and the mechanism they suggest (wards staffing up for the full moon) need not exist in the US. The essay is Halloween moves births. The full moon does not.