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Recent research proposed an efficient way of streamlining p-hacking: a metric of randomly generated p-values that always indicates significant results (p-ointless). However, the metric’s applicability is limited, since it covers only a very small range of use cases that are relevant for p-hacking researchers. To address this issue, we present extra p-ointless, a new metric that allows researchers to freely specify desired p-value ranges while safeguarding full reproducibility. Our newly introduced extra p-ointless metric not only addresses the limitations of the original metric but also answers editors’ and reviewers’ calls for alternative and innovative statistical approaches. An R-based, interactive shiny web app helps researchers with little background in coding to apply our metric.
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Stored only when both papers are in the corpus — 9 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.