  Statisticians are largely focused on developing methods that perform well in
a frequentist sense -- even the Bayesians. But the widely-publicized
replication crisis suggests that these performance guarantees alone are not
enough to instill confidence in scientific discoveries. In addition to reliably
detecting hypotheses that are (in)compatible with data, investigators require
methods that can probe for hypotheses that are actually supported by the data.
In this paper, we demonstrate that valid inferential models (IMs) achieve both
performance and probativeness properties and we offer a powerful new result
that ensures the IM's probing is reliable. We also compare and contrast the
IM's dual performance and probativeness abilities with that of Deborah Mayo's
severe testing framework.
