> Your hypothesis is: layout influences signup behavior.

This might be your hypothesis but this isn't the hypothesis that the p-value is related to.

>Setting a p-value threshold of 0.05 is equivalent to saying: "I’m willing to accept a 5% chance of shipping something that only looked good by chance."

P-values don't provide any information about "chance occurrence" but rather they test the probability of observing a particular outcome assuming a particular state of the world (i.e. the null hypothesis).

> Bonferonni

Besides being a very aggressive (i.e. conservative) correction, I would imagine that in industry, just as in science, the motivations to observe results will mean it wont be used. There are other way more reasonable corrections.

> Avoid digging through metrics post hoc

The reasonable solution is not to ignore the results that you found but to interpret them appropriately. Maybe the result actually does signal improved intention - alternatively maybe it is noise. Treat it as exploratory data. If that improved retention was real, is this important? Important enough to appropriately retest for?