Synthese invites submissions on severity, error detection, and how scientific and other forms of inquiry learn from error across disciplines.
The collection explores a fundamental principle of evidence: a claim is not well-tested or epistemically warranted when it is based on a method that would have made it easy to accept the claim even if it were false. The collection seeks to investigate how claims earn warrant by surviving probes capable of revealing their errors or inadequacies.
Contributions may develop, apply, or challenge severity-based reasoning, or propose alternative approaches—including Bayesian, frequentist, machine-learning, and other frameworks—that address how inquiry learns from error.
Topics include:
The collection is partly motivated by the thirtieth anniversary of Deborah Mayo’s Error and the Growth of Experimental Knowledge (1996) and the subsequent development of its account of severe testing.
Submissions should be made through the Synthese Editorial Manager. Under the drop-down menu, select “Severity and Learning from Error.”
Submitted papers will undergo the usual Synthese review process.
15 December 2026