 So EU theory or Bayesian decision theory underpins a powerful set of epistemic norms. It has been taken as the appropriate account of scientific inference, giving rise to a school of statistical inference and experimental design and inviting formal interpretations of key concepts like “evidence”, “evidential support”, “induction” versus “abduction”, and the bearing of “coherence” and “explanatory power” on truth (see the relevant  related entries).  The major competitor to Bayesianism, as regards scientific inference, is arguably the collection of approaches known as Classical or Error statistics, which deny the sense of “degrees of support” (probabilistic or otherwise) conferred on a hypothesis by evidence. These approaches focus instead on whether a hypothesis has survived various “severe tests”, and inferences are made with an eye to the long-run properties of tests as opposed to how they perform in any single case, which would require decision-theoretic reasoning (see the entry on  philosophy of statistics).