significancerequires

Significancerequires is a coined term used in research methodology to describe a normative standard for claims of significance. It emphasizes that declaring a result significant should rest on more than a single statistical threshold; significance, in this view, is a composite requirement comprising several criteria that collectively support a robust conclusion.

Core components of significancerequires include statistical metrics, practical relevance, and methodological safeguards. Researchers are encouraged to report p-values alongside effect sizes and confidence intervals, and to consider study power and model assumptions. In addition, preregistration of hypotheses and analytical plans, as well as corrections for multiple testing, are often invoked to prevent p-hacking. Replicability and external validity are also treated as part of the standard, with significance strengthened by consistent results across independent samples or datasets.

In practice, applying significancerequires guides how results are communicated. Authors should present comprehensive statistical information, justify the relevance of the effect in context, disclose data and analysis methods when possible, and acknowledge limitations. Journals and funders increasingly adopt related policies that align with this principle, promoting transparency, preregistration, and data sharing to improve interpretability and reproducibility.

Critics of the term caution that it can be ambiguously defined and may lead to rigid criteria that exclude scientifically meaningful findings, especially in exploratory research or fields with nuanced effect patterns. Proponents argue that a principled, multifaceted approach to significance helps mitigate overreliance on arbitrary thresholds and supports more reliable evidence synthesis. Related concepts include statistical significance, effect size, preregistration, p-hacking, and reproducibility.