Nullius in Verba

In this episode, we will reflect on how governments should support science, from the 1945 policy report Science: The Endless Frontier by Vannevar Bush, and then move on to the recent policy document by the Trump Administration 'Science: A New Golden Age'. We reflect on what we like about the policy report and what we like less. Is the linear model from basic research to application indeed outdated? Are metascience units a good idea? And will AI accelerate and radically transform how we do science? Enjoy.

Shownotes:

Bush, Vannevar. (1945). Science The Endless Frontier. http://archive.org/details/in.ernet.dli.2015.212068

Bush, Vannevar. (1970). Pieces of the action. New York, Morrow. http://archive.org/details/piecesofaction00bush

Science: A Golden Age https://www.whitehouse.gov/wp-content/uploads/2026/07/Science-A-New-Golden-Age.pdf

In this episode, we will discuss multiverse analysis–what it is, why we use it, and how we should use it. We also discuss many analyst projects, and Daniel's new preprint on multiverse analysis. 

 

Shownotes

Auspurg, K. (2025). Robustness is better assessed with a few thoughtful models than with billions of regressions. Proceedings of the National Academy of Sciences, 122(43), e2521917122.

Chamberlin, T. C. (1890). The method of multiple working hypotheses. Science, (366), 92–96.

Dochtermann, N. A., & Jenkins, S. H. (2011). Developing multiple hypotheses in behavioral ecology. Behavioral Ecology and Sociobiology, 65(1), 37–45.

Dreber, A., & Johannesson, M. (2025). The credibility gap: Evaluating and improving empirical research in the social sciences. Routledge.

Gelman, A., & Loken, E. (2013). The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time. Department of Statistics, Columbia University, 348(1-17), 3.

Knief, U., & Forstmeier, W. (2025). Are our 95% CIs only worth 45% confidence? bioRxiv, 2025-08.

Lakens, D., Rasti, S., & Tunç, M. N. (2026). There is only one correct analysis. (Preprint)

Platt, J. R. (1964). Strong Inference: Certain systematic methods of scientific thinking may produce much more rapid progress than others. Science, 146(3642), 347–353.

Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2020). Specification curve analysis. Nature Human Behaviour, 4(11), 1208–1214.

Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing transparency through a multiverse analysis. Perspectives on Psychological Science, 11(5), 702–712.

Wimsatt, W. C. (2007). Re-engineering philosophy for limited beings: Piecewise approximations to reality. Harvard University Press.

After a short break due to busy calendars, we are back to discuss the role automation and artificial intelligence can play in science. Is AI evil, or does it just amplify the problems that were already present in science? Can automated AI driven checks help scientists to improve the way they work? If so, where are these tools helpful, and when? Enjoy. 

The Babbage quote is from Babbage in November 1839, recalling events in 1821; quoted in Harry Wilmot Buxton and Anthony Hyman (1988), Memoir of the Life and Labours of the Late Charles Babbage.

 

Alfred Whitehead: An Introduction to Mathematics (1911)

 

Blog about AI references: Evaluating Dr. Cuddy’s Claim that the Debunking of Power Posing is a Myth. https://daniellakens.blogspot.com/2026/05/evaluating-dr-cuddys-claim-that.html

 

Metacheck: DeBruine L, Mesquida C, Werner J, Lakens D (2026). metacheck: Check Research Outputs for Best Practices. doi:10.5281/zenodo.20704754, R package version 0.1.0, https://scienceverse.org/metacheck

 

Cummins, J., Clarke, B., Hussey, I., & Elson, M. (2026). RegCheck: A tool for structured comparisons between study registrations and papers (arXiv:2601.13330). arXiv. https://doi.org/10.48550/arXiv.2601.13330

In this episode, we continue our discussion of disagreement in science, shifting the conversation from why it matters to how to do it well.

 

Shownotes

Paul Graham. (2008). How to disagree. 

Rapoport's Rules. 

Cass Sunstien. The Rapoport Rules.

Preregistration is redundant, at best. 

An Evidence-Based Critique of the Cass Review 

Fiedler, K., Messner, C., & Bluemke, M. (2006). Unresolved problems with the “I”, the “A”, and the “T”: A logical and psychometric critique of the Implicit Association Test (IAT). European Review of Social Psychology, 17(1), 74–147. https://doi.org/10.1080/10463280600681248

Neyman, J. (1961). Silver Jubilee of My Dispute With Fisher. Journal of the Operations Research Society of Japan, 3, 145–154.

This is a two-part episode on the role of disagreement in science. In the first part, we discuss the "why," before moving on to the "how" in the next episode. Enjoy. 

 

Shownotes

Dellsén, F., & Baghramian, M. (2021). Disagreement in science: Introduction to the special issue. Synthese, 198(Suppl 25), 6011-6021.

Oreskes, N., & Conway, E. M. (2011). Merchants of doubt: How a handful of scientists obscured the truth on issues from tobacco smoke to global warming. Bloomsbury Publishing USA.

Popper, K. (1959). The Logic of Scientific Discovery. London: Hutchinson.

Seidel, M. (2021). Kuhn’s two accounts of rational disagreement in science: an interpretation and critique. Synthese, 198(25), 6023-6051.

Shaw, J. (2021). Feyerabend and manufactured disagreement: reflections on expertise, consensus, and science policy. Synthese, 198(25), 6053-6084.