I’m responding to all significant comments by Martin Robbins, Richard Tol, and Jonathan Jones below (other comments were not relevant, or do not accurately represent the paper, my comments or Grimes’ comments):

1) I am not aware of anyone having applied a homogeneous, or non-homogeneous, Poisson process to conspiratorial ideation, nor has it come up in the review process or in the scrutiny since publication. Therefore, the application of this classic piece of mathematics appears to be very novel. (If in fact it has been done, the literature has apparently been so thoroughly forgotten that it deserves to be re-popularized, which is what Grimes’ paper does very effectively.) The mathematics of a paper need not be novel if the application of the mathematics is novel, which appears to be the case here. Poisson cannot have applied his model to Moon Landing or Climate Change conspiracy ideation. Science is full of examples of valid research applying well-known mathematics in new contexts. Insisting that all new science involve new mathematics, as some of the commentators appear to be saying, would mean hardly any science would ever get done. Grimes had the bright idea to realize that conspiracy ideation could be evaluated through failure modelling.
2) The conclusions in the paper rested on the constant population case, which correctly uses the homogeneous Poisson process model and are therefore not affected by the error.
3) Correcting the error has the effect of increasing the probability that a conspiracy is detected, in situations where one might wish to apply a non-homogeneous Poisson process. The reason this flaw did not alter the conclusions is that the flaw necessarily renders the analysis conservative. It’s as simple as that.
4) Expecting a citation to Box or Poisson when you use a Poisson model is like expecting a citation to Leibniz every time you integrate an equation. Perhaps desirable, but not strictly necessary and not a flaw to prevent publication.
5) While only three probabilities p could be generated from the empirical examples: that’s an improvement on the current number of estimates (i.e., none); Grimes conservatively uses the lowest estimated value in his analysis; and the paper includes a clear discussion of the limitations arising from the difficulty in estimating p. Future work could get more values of p.