Most important is that a rejection of nullhypothesis of the pcurve analysis does not tell you WHY all true effect sizes do not all appear to be zero... this can be for different reasons i-iii

how large this probability/power is depends on the strength of the effect size of statistic in (i), the amount of p-hacking in (ii), amount of effect size heterogeneity in (iii), number of statistics in the analysis, and relative number of statistics in the p-curve analysis affected by (i)-(iii).

(i)-(iii) lead to a rejection of the null-hypothesis of the p-curve analysis with a probability larger than alpha (the power of the test)...

The null is false if at least one mu <> 0. This may occur if (i) the null is false for only one statistics (this may be an outlier), (ii) p-hacking or something else affects the distribution of one or more statistics, or (iii) effect size heterogeneity (can be seen as a special case of (i)).

Ha!

This is, fortunately, not rocket science.

P-curve tests the hypothesis that ALL test statistics arise from H0, for instance, mu = 0).