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.

翻译：统计学家主要致力于开发在频率学派意义上表现良好的方法——即使是贝叶斯学派也不例外。但广为人知的可重复性危机表明，仅凭这些性能保证不足以增强对科学发现的信心。除了可靠地检测与数据（不）兼容的假设外，研究者还需要能够探测实际受数据支持的假设的方法。本文证明，有效推断模型（IM）同时具备性能与探究性属性，并提出一项有力的新结果，确保IM的探测过程是可靠的。我们还比较了IM的双重性能与探究能力与Deborah Mayo的严格检验框架之间的异同。