Science1 distinct publisher2 min readPublished
Jeffrey Rosenthal and Jennifer Dmetrichuk found no excess of unexpected deaths on the date across 15 years of Ontario records. What only 27 qualifying days can actually exclude is the more useful number.
The Scientist · Science desk

Compiled by The ScientistSomething wrong?How this is made
Count the days actually on trial. The calendar put 27 Friday the 13ths between Jan. 1, 2009 and Dec. 31, 2023 [1], inside a window of 5,478 days [2]. A file of more than 250,000 unexpected deaths averages about 46 a day across that window [4], which places roughly 1,230 deaths in the exposed group [3].
That number sets the resolution. Counting noise on a group of 1,230 is near its square root, about 35 deaths, so a real difference has to clear something like 70 before it separates from chance, about 5.7 percent of the baseline [5]. An excess of one death in twenty on Friday the 13th would have surfaced in this design; an excess of one in fifty would not.
The contrast, as Rosenthal describes it, was between Friday the 13th and the overall rate on all other days [6]. Fridays are not interchangeable with Tuesdays for accidental death, and pooling every non-13th day folds that weekday difference into the comparison in the direction that would flatter the superstition, because every exposed day here is a Friday. The count still came in a shade below the rest of the calendar [6]. Holding weekday fixed, Fridays against other Fridays, or the 13th of each month against the 6th and the 20th, would isolate the date itself.
The interview, not the data, explains why the belief survives arithmetic. Rosenthal, who found out from a calendar program he wrote himself that he was born on a Friday the 13th [11], offers Apollo 13, which suffered its explosion on April 13, 1970, a Monday [8]. It gets filed as evidence for a Friday superstition anyway, which is confirmation bias doing ordinary work: the memorable case is recruited and the inconvenient weekday quietly drops out [7].
The paper also looked at full moons, Halloween and time of year, and the interview reports a finding for none of the three [12]. The moon is the better-powered test. A full moon comes round roughly 12.4 times a year, so about 185 exposed days sit inside the same 15-year window, near 8,440 expected deaths, and the same noise calculation bounds a lunar effect at roughly 2.2 percent [6]. If those numbers land where the Friday numbers did, the lunar null is the stronger of the two, and it is the one worth reading in the paper itself.
Ranked by verification strength, evidence, and original report placement.
The phys.org interview reports the numerical result only for Friday the 13th; it states that the paper also examined full moons, Halloween and times of year but gives no findings for those.
Jeffrey Rosenthal published a paper in the journal Chance with co-author Jennifer Dmetrichuk exploring the widely held notion that bad events are more likely to occur on Friday the 13th; the pair also looked at the effect of full moons, Halloween and times of year.
Jeffrey Rosenthal is a professor of probability and statistical computing in the department of statistical sciences, Faculty of Arts & Science, University of Toronto.
Jennifer Dmetrichuk is an assistant professor in the department of laboratory medicine and pathobiology at the Temerty Faculty of Medicine and a forensic pathologist and regional supervising coroner in Toronto.
The study looked at data for all unexpected deaths in Ontario for the 15-year period from 2009 to 2023, including deaths from homicide, accidents and unexpected natural deaths, which Rosenthal describes as a good measure of something bad happening or bad luck.
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Evidence-backed comparisons of source perspectives and observed adoption signals. Read the methodology
Which Builder, Operator, and Investor concerns the observed source mix emphasized—not a truth score.
Evidence, demonstrated adoption, hype gap, incentives, and confidence are assessed independently, each on its own current evidence. How these are measured.
One author, no paper, no figures
This account comes from a single conversation with one of the two authors, arranged by the University of Toronto's own writer and picked up by phys.org. The Chance paper itself stays out of view, and the interview offers no counts and no interval, its strongest quantitative phrase being 'a tiny bit less'. What keeps this off the floor is that the stated inputs are specific enough to check: the jurisdiction, the 2009-2023 window and the 250,000-plus deaths reconstruct into arithmetic that holds up.
No uptake to count
A null finding in a statistics magazine gives little for anyone to build on. Weeks after publication, the story stands alone, with no other outlet picking it up and no sign of anyone putting the result to use, which fits the short timeframe involved.
'Proof' outruns 27 days of exposure
Rosenthal calls the result 'pretty good scientific proof that all those fears are really not justified', and phys.org carries that wording unqualified. On roughly 1,230 expected deaths across the qualifying dates, chance alone moves the count by about 35 either way, so a shift of two or three percent would sit invisibly inside the noise. What the data genuinely exclude is a Friday the 13th that is appreciably deadlier than other days, and no reader of this interview is given the means to tell those two statements apart.
House organ, with books on the shelf
Chris Sasaki, a University of Toronto writer, is interviewing a University of Toronto professor, and the piece doubles as a plug: Rosenthal gets to mention his two probability titles and to frame scientific literacy as his aim. None of that tilts a null result, since a debunking serves the same purpose either way. It does explain the confident wording and the absence of any account of what the study could not have seen.
Modest claim, checkable arithmetic, unseen paper
A null on a calendar superstition is the least surprising outcome available, and the study's stated dimensions let its resolution be reconstructed to within a rounding error, so this assessment leans on arithmetic rather than on trust. What stays unknown is whether the paper itself reports the sensitivity limit the interview omits, and that single fact is what would move this number.