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Hypotheses are central to information acquisition, decision-making, and discovery.
On the use and interpretation of certain test criteria for purposes of statistical inference part i
Neyman, J. and Pearson, E. S · 1928
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The testing of statistical hypotheses in relation to probabilities a priori
Neyman, J. and Pearson, E. S · 1933
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Design of experiments
Fisher, R. A · 1936
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The Logic of Scientific Discovery
Popper, K · 1959
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The Structure of Scientific Revolutions
Kuhn, T. S · 1962
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Statistical methods for research workers
Fisher, R. A · 1970
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400: A method for combining non-independent, one-sided tests of significance
Brown, M. B · 1975
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The Methodology of Scientific Research Programmes
Lakatos, I · 1978
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The Scientific Image
van Fraassen, B. C · 1980
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Fact, Fiction, and Forecast
Goodman, N · 1983
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Controlling the false discovery rate: a practical and powerful approach to multiple testing
Benjamini, Y. and Hochberg, Y · 1995
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Why most published research findings are false
Ioannidis, J. P · 2005
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The logic of scientific discovery
Popper, K · 2005
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Theory and reality: An introduction to the philosophy of science
Godfrey-Smith, P · 2009
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Normal science and dogmatism, paradigms and progress: Kuhn ‘versus’ popper and lakatos
Press, C. U · 2009
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Popper, kuhn, lakatos and aim-oriented empiricism
Maxwell, N · 2012
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Popper and his popular critics: Thomas kuhn, paul feyerabend and imre lakatos
Agassi, J · 2014
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Estimating the reproducibility of psychological science
Collaboration, O. S · 2015
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The new nhgri-ebi catalog of published genome-wide association studies (gwas catalog)
MacArthur, J., Bowler, E., Cerezo, M., Gil, L., Hall, P., Hastings, E., Junkins, H., McMahon, A., Milano, A., Morales, J., et al · 2017
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The biogrid interaction database: 2019 update
Oughtred, R., Stark, C., Breitkreutz, B.-J., Rust, J., Boucher, L., Chang, C., Kolas, N., O’Donnell, L., Leung, G., McAdam, R., et al · 2019
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The language of betting as a strategy for statistical and scientific communication
Shafer, G · 2019
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Selective inference: The silent killer of replicability
Benjamini, Y · 2020
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The gtex consortium atlas of genetic regulatory effects across human tissues
Consortium, G · 2020
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Safe testing
Grünwald, P., de Heide, R., and Koolen, W. M · 2020
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A large-scale benchmark for few-shot program induction and synthesis
Alet, F., Lopez-Contreras, J., Koppel, J., Nye, M., Solar-Lezama, A., Lozano-Perez, T., Kaelbling, L., and Tenenbaum, J · 2021
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E-values: Calibration, combination and applications
Vovk, V. and Wang, R · 2021
Litsearch: A retrieval benchmark for scientific literature search, 2024
Ajith, A., Xia, M., Chevalier, A., Goyal, T., Chen, D., and Gao, T · 2024
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Baek, J., Jauhar, S. K., Cucerzan, S., and Hwang, S. J · 2024
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Marg: Multi-agent review generation for scientific papers, 2024
D’Arcy, M., Hope, T., Birnbaum, L., and Downey, D · 2024
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Large language models are not strong abstract reasoners, 2024
Gendron, G., Bao, Q., Witbrock, M., and Dobbie, G · 2024
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Blade: Benchmarking language model agents for data-driven science, 2024
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Hypothesis formalization: Empirical findings, software limitations, and design implications
Jun, E., Birchfield, M., De Moura, N., Heer, J., and Just, R · 2022
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Crispr activation and interference screens decode stimulation responses in primary human t cells
Schmidt, R., Steinhart, Z., Layeghi, M., Freimer, J. W., Bueno, R., Nguyen, V. Q., Blaeschke, F., Ye, C. J., and Marson, A · 2022
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False discovery rate control with e-values
Wang, R. and Ramdas, A · 2022
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Inductive reasoning in humans and large language models, 2023
Han, S. J., Ransom, K., Perfors, A., and Kemp, C · 2023
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Instruction induction: From few examples to natural language task descriptions
Honovich, O., Shaham, U., Bowman, S. R., and Levy, O · 2023
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A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al · 2023
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Gu, K., Shang, R., Jiang, R., Kuang, K., Lin, R.-J., Lyu, D., Mao, Y., Pan, Y., Wu, T., Yu, J., Zhang, Y., Zhang, T. M., Zhu, L., Merrill, M. A., Heer, J., and Althoff, T · 2024
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Autonomous llm-driven research from data to human-verifiable research papers, 2024
Ifargan, T., Hafner, L., Kern, M., Alcalay, O., and Kishony, R · 2024
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Lehr, S. A., Caliskan, A., Liyanage, S., and Banaji, M. R · 2024
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The ai scientist: Towards fully automated open-ended scientific discovery, 2024
Lu, C., Lu, C., Lange, R. T., Foerster, J., Clune, J., and Ha, D · 2024
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Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al · 2024
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Discoverybench: Towards data-driven discovery with large language models
Majumder, B. P., Surana, H., Agarwal, D., Mishra, B. D., Meena, A., Prakhar, A., Vora, T., Khot, T., Sabharwal, A., and Clark, P · 2024
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Automated social science: Language models as scientist and subjects, 2024
Manning, B. S., Zhu, K., and Horton, J. J · 2024
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Citeme: Can language models accurately cite scientific claims?, 2024
Press, O., Hochlehnert, A., Prabhu, A., Udandarao, V., Press, O., and Bethge, M · 2024
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Qiu, L., Jiang, L., Lu, X., Sclar, M., Pyatkin, V., Bhagavatula, C., Wang, B., Kim, Y., Choi, Y., Dziri, N., and Ren, X · 2024
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Code generation with alphacodium: From prompt engineering to flow engineering
Ridnik, T., Kredo, D., and Friedman, I · 2024
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Can llms generate novel research ideas? a large-scale human study with 100+ nlp researchers, 2024
Si, C., Yang, D., and Hashimoto, T · 2024
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Scicode: A research coding benchmark curated by scientists, 2024
Tian, M., Gao, L., Zhang, S. D., Chen, X., Fan, C., Guo, X., Haas, R., Ji, P., Krongchon, K., Li, Y., Liu, S., Luo, D., Ma, Y., Tong, H., Trinh, K., Tian, C., Wang, Z., Wu, B., Xiong, Y., Yin, S., Zhu, M., Lieret, K., Lu, Y., Liu, G., Du, Y., Tao, T., Press, O., Callan, J., Huerta, E., and Peng, H · 2024
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Massw: A new dataset and benchmark tasks for ai-assisted scientific workflows, 2024
Zhang, X., Xie, Y., Huang, J., Ma, J., Pan, Z., Liu, Q., Xiong, Z., Ergen, T., Shim, D., Lee, H., and Mei, Q · 2024
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Hypothesis generation with large language models
Zhou, Y., Liu, H., Srivastava, T., Mei, H., and Tan, C · 2024
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Imre lakatos’ approach: Bridging popper and kuhn in philosophy of science, 2023
Philosophy Institute · 2025
Closest in time.
The replication crisis is less of a ”crisis” in lakatos’ philosophy of science
Rubin, M · 2025
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