Calibrating p‐values in ecology: a practical framework for integrating prior plausibility into statistical inference

Misinterpretation of p‐values, coupled with insufficient consideration of the prior plausibility of ecological hypotheses, leads to overconfident and often unreliable inference in ecological research. To address this issue, we present a methodological framework for p‐value calibration that reinterprets conventional p‐values through minimum Bayes factors to quantify evidential strength given the prior probability of a hypothesis. Using trophic cascade experiments as a case study, we demonstrate how identical p‐values can correspond to different posterior probabilities depending on prior plausibility, and we provide practical tools for applying calibration in ecological analyses. This approach requires only standard p‐values and a qualitative or quantitative assessment of prior evidence, making it readily applicable to both existing and new studies. We also outline a structured framework for assigning prior probabilities based on theoretical and meta‐analytic information, and show how calibration can help reduce false‐positive claims, promote replication, and better align statistical inference with ecological theory. This work promotes a pragmatic, easily implemented method that bridges current frequentist practice and Bayesian reasoning, with the goal of strengthening the reliability and interpretation of ecological research.