In a journal article, there can be dozens of statistical tests that are unrelated, and, the more tests there are, the greater the likelihood that the probabilities for one of those tests for a given type I error would convert from $p \lesssim0.05$ to $p \gtrsim0.05$ (or the obverse) with a different initial data set. Yet, no one has ever suggested doing a Bonferroni correction on an entire suite of unrelated statistical tests, just because a journal article has a larger or smaller number of statistical tests in its text. This is because it is a non-issue as journal articles are not statistical units (apples and oranges). The strategy I use for this is to regard probabilities with a somewhat jaundiced eye, that is, I consider probabilities that are $0.02<p<0.1$ or $0.01<p<0.2$, depending on the context as borderline, and not strongly indicative of anything. I would be interested to hear what my colleagues on this site think of that approach. I am most interested in hearing what my colleagues think about probabilities in the neighborhood of $0.05$, which I do not think is reliable enough to be strongly indicative of anything, that is, without knowing also how variable that probability would be in a repeat experiment.
Also see https://stats.stackexchange.com/a/33825/99274 which gives an example for $\alpha=0.05$. So, shouldn't we be examining reliability for probabilities? This is also relevant to current concerns, e.g., Is this the solution to the p-value problem?