Can multiple testing be done sensibly at scale?

Modern experiments produce millions of hypotheses (genomics, neuroimaging, A/B testing). Bonferroni is too strict; FDR is popular but interpretation is subtle; e-values are emerging.

How to balance discovery and false positives when tests are correlated, when there is selective inference, or when test statistics are themselves chosen post hoc, is unsolved.