significanceboth

Significanceboth is a concept that has emerged in interdisciplinary research to describe a finding that simultaneously satisfies two distinct criteria of importance. Traditionally, “significance” in statistics refers to a result that is unlikely to have occurred by chance according to a pre‑specified p‑value or confidence interval. In applied fields such as psychology, economics, and biomedical science, researchers increasingly care about whether a statistically significant effect also carries practical or clinical weight. The term significanceboth captures this dual requirement: a result must be statistically reliable **and** have a meaningful magnitude or real‑world impact.

The notion appears in discussions of “practical significance” or “clinical significance.” For instance, a drug may show a statistically significant reduction in blood pressure, but if the reduction is only a few millimetres of mercury it may not alter patient management. A significantboth assessment would demand that the effect size exceeds a threshold that translates into a tangible benefit, often expressed through measures such as Cohen’s d, odds ratios, or absolute risk reduction.

Significanceboth is frequently operationalised in reporting guidelines. The American Psychological Association’s “Statistical Significance and Effect Size” framework encourages authors to present both p‑values and effect‑size indices. In health‑tech regulation, agencies like the FDA require that clinical trial results demonstrate significanceboth, ensuring that approved interventions are both statistically sound and clinically relevant.

While not yet codified as a formal statistical test, the principle of significanceboth guides transparent research evaluation and communication. Scholars advocate its use to prevent over‑interpretation of marginally significant findings and to encourage the prioritisation of outcomes that matter to stakeholders.

Conceptually, significanceboth belongs to a broader movement that urges scientists to report results in ways that are simultaneously rigorous, replicable, and useful for decision‑making.