The contemporary scientific landscape is navigating a significant statistical crisis, marked by the widespread misapplication of statistical methodologies and a deep-rooted reliance on point null hypotheses. This has led to pervasive issues such as the zero probability paradox and the Jeffreys-Lindley paradox, creating a discordance between frequentist and Bayesian statistical inference methods. Addressing these challenges, our manuscript advocates for a paradigm shift toward the adoption of interval null hypotheses, focusing on effects of practical significance. This approach introduces “contextual significance,” a concept that transcends the traditional reliance on low p-values as sole indicators of evidence, advocating for a nuanced and context-sensitive method in hypothesis testing. Through analysis of the fundamental causes of the statistical crisis, including p-hacking, publication bias, and improper application of statistical techniques, we underscore the need for transparent statistical methodologies. Our proposal includes comprehensive reforms in statistical education, promoting reproducibility through open science practices, and advancing statistical technologies. The manuscript culminates in a call for a paradigmatic shift to interval null hypotheses, aiming to resolve key paradoxes and reconcile differences in statistical approaches. This transformative change, supported by a consortium of statisticians, educators, and journals, signifies a crucial step toward reinforcing the integrity and progress of scientific research.