Sorry to report that the only aspect of this discussion that makes sense to me is the comment that seasonality is at play here, and the note that the data are not about astrology; they are simply about astrological month of birth, so that the chi-squared analysis makes no sense given the cyclical ordering.
Turning to biology, the chi-squared analysis fares even worse given the astronomical (as opposed to astrological) irrelevance of the month categories (there are after all about 12.4 lunar-phase cycles and 13.3 lunar-orbit cycles a year). That aside, lifetime effects of season of gestation are very plausible based on the embryology and life-course epidemiology I know of; for example sunlight exposure affects vitamin D levels which are central to bone formation and maintenance, as well as maternal resistance to infections which in turn can have dramatic effects on the fetus that last a lifetime (e.g., impaired hearing from congenital rubella). And infections themselves can have dramatic seasonal patterns, even in the tropics.
To model seasonality in a way connected to the leading hypothetical mechanisms (gestational nutrition, infections, and sunlight) I would instead start with a 2-parameter single-annual cycle on the circle of months: one parameter for the cycle origin (circle location) and another that distorts away from the sinusoidal shape via horizontal stretching and compression. Other parameters essential for serious seasonality analysis would include monotone interactions with birth year (recognizing that seasonal nutrition and infection have been flattened out by modern food distribution and infection controls); and some sort of climate interaction to allow for sunlight exposure and respiratory infections. These items compose the qualitative prior information represented by the data model (as per Box 1980).
For an analysis of astrology, those seasonal terms would be needed as potential confounders. A fair analysis would also group the signs by alleged sign characteristics or some other aspect of astrological theory. The latter is quite elaborate according to some afficionados and includes lunar-cycle elements (I donât know details and I would consult with those who do).
This is all to say: I donât believe in astrology any more than others here, but the knee-jerk âthis is noiseâ responses to âsignificantâ analyses purporting to be about astrology betray how statistics has its own severe problems with irrational belief and sheer prejudice passed off as scientific skepticism, as often imposed by âskepticalâ priors. That prejudice is most prominent in declarations that a priori an association must be random if you canât immediately think of a plausible mechanism for the association. Such imagination failure usually happens because you donât know the background topic well enough to realize the many not-unreasonable ways the association could be ârealâ (i.e., due to a causal mechanism, albeit perhaps not the one under study).
I find it particularly disturbing when commentators double down on their ignorance with claims that there are data proving there is no association (perhaps because they read reviews saying there is none, based on all the underpowered studies that reported p>0.05 as âno associationâ), having never bothered to dispassionately analyze the actual background literature. This pseudo-empirical pseudo-skepticism is one version of what I call nullism; it plagues the âreproducibility crisisâ industry as transported from experimental psychology into medical epidemiology, where it has been weaponized for the risk-denial industry.
BTW my jaundiced view of pseudoscientific prejudice passed off as skepticism is derived from one of Feyerabendâs themes, which he illustrated using astrology as an example. And no he didnât believe in astrology, he just thought most scientists attacking it were hypocritical as âscience defendersâ, given their sloppy, prejudicial refutations.