SignificanceEG

SignificanceEG is a proposed statistical concept intended to broaden the communication of research results by combining traditional statistical significance with an assessment of practical or theoretical importance. The term has appeared in methodological discussions and is not part of a formal, widely adopted standard.

Conceptually, SignificanceEG includes several components: (1) the result's statistical significance (typically a p-value or posterior probability), (2) the size and precision of the estimated effect (effect size and confidence interval), and (3) an assessment of contextual relevance or explanatory gain within the study domain. The 'EG' in SignificanceEG is commonly treated as a qualitative or semi-quantitative grading of how much new understanding the result provides, or how likely the finding is to affect decision-making.

Interpretation and use: In practice, SignificanceEG would yield a composite score or a narrative index that observers could use alongside traditional metrics. It is intended to help readers assess not only whether an effect exists but whether it matters for theory, policy, or practice. Some researchers propose dashboards or scoring rubrics to report SignificanceEG alongside p-values and confidence intervals.

Reception and limitations: Critics argue that SignificanceEG risks introducing subjectivity through its contextual weighting and could obscure uncertainty if not clearly defined. Others note that it may duplicate information already conveyed by effect sizes and Bayesian factors. Proponents contend that when used transparently, it can improve interpretability and decision-making.

Relation to related concepts: SignificanceEG is related to practical significance, clinical significance, and statistical decision theory. It differs by aiming to couple a formal numeric or qualitative significance assessment with domain-specific relevance in a single framework.