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P-values: misunderstood and misused

, and . (January 2016)cite arxiv:1601.06805Comment: Submitted to Frontiers in Physics, Interdisciplinary Physics, "Research Topic": At the crossroads: lessons and challenges in Computational Social Science.

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SemEval-2023 Task 10: Explainable Detection of Online Sexism., , , and . SemEval@ACL, page 2193-2210. Association for Computational Linguistics, (2023)Risks and Opportunities of Open-Source Generative AI., , , , , , , , , and 15 other author(s). CoRR, (2024)The PRISM Alignment Project: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models., , , , , , , , , and 2 other author(s). CoRR, (2024)Improving the Detection of Multilingual Online Attacks with Rich Social Media Data from Singapore., , , , , , and . ACL (1), page 12705-12721. Association for Computational Linguistics, (2023)SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model Safety., , , and . CoRR, (2024)The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources., , , , , , , , , and 13 other author(s). CoRR, (2024)Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI., , , , , , , , , and 14 other author(s). ICML, OpenReview.net, (2024)Dynabench: Rethinking Benchmarking in NLP., , , , , , , , , and 9 other author(s). NAACL-HLT, page 4110-4124. Association for Computational Linguistics, (2021)Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection., , , and . ACL/IJCNLP (1), page 1667-1682. Association for Computational Linguistics, (2021)Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection., , , and . CoRR, (2020)