Nature Human Behaviour - Claims about scientific rigour require rigour
Andreas De Block on Twitter / X
It took Nature three years to publish this rebuttal, which clearly demonstrates the fundamental flaws in the original paper. In the meantime, the paper's conclusions influenced scientific debate and policy decisions. Such delays in correcting flawed research do real damage. https://t.co/QI3Ei3PE70— Andreas De Block (@DeblockBlock) August 13, 2026
Research ethics: 3 ways to blow the whistle
Reporting suspicions of scientific fraud is rarely easy, but some paths are more effective than others.
The natural selection of bad science
Abstract. Poor research design and data analysis encourage false-positive findings. Such poor methods persist despite perennial calls for improvement, sugg
Why Science Can't Settle Political Disputes
Attempts to scientifically “rationalize” policy, based on the belief that science is purified of politics, may be damaging democracy
The High Cost of Not Doing Experiments - Behavioral Scientist
We can pay dearly, in blood, treasure, and well-being, for experiments that aren’t done. -Richard Nisbett, Mindware
Nature is expanding Registered Reports to all the fields in which we publish
Registered Reports improve the credibility of scientific claims by rewarding big questions, sound methods and solid analyses. They need to become a standard tool in research.
Guest post: If you’re going to critique science, be scientific about it
Loren K. Mell Editor’s note: This post responds to a Feb. 13 article in The Atlantic, “The Scientific Literature Can’t Save Us Now,” written by Retraction Watch cofounders Adam Marcus and Ivan Oran…
Are Scientific Papers Bad?
New Studies: How Commercial Forces Make Science Less Reliable
Computational social science has been distorted by commercial forces, and AI is making it worse.
Why Most Published Research Findings Are False
Summary There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.
Is the scientific paper still a fraud?
How we write scientific papers does not reflect how we do science. Their formal structure infers a pre-ordained linear process rather than reflecting the messy creativity of research. This matters in the AI age because it masks the human in the process.
Verification in the Age of AI Scientists
AI Scientists now operate at a scale that outpaces human capacity for manual review. Systems such as Sakana’s AI Scientist write entire workshop papers end-to-end. Lila Sciences runs autonomous ``AI Science Factories’’ that hypothesize, experiment, and iterate without human guidance. FutureHouse’s Kosmos and Robin generate thousands of candidate hypotheses in a single run, and Google’s Co-Scientist proposes testable experiments at a rate no laboratory can fully evaluate. Each of these systems emphasizes verified results, yet that standard ranges from near-perfect formal proof in mathematics to decade-long clinical trials in medicine, with no shared framework for judging sufficiency across domains. As outputs scale beyond what humans can manually inspect, the question of which results to trust becomes as hard as generating them. The bottleneck for AI for Science is no longer hypothesis generation, it is verification.
Dark-Moneyed Denialists Are Running 'Fixing Science' Symposium of Doubt
The “Fixing Science” symposium, which is hosted by the National Association of Scholars and kicks off today in Oakland, California, includes credible speakers who want to improve some areas of science hurt by the use of poor statistical methods or making irreproducible claims. Unfortunately, they are outnumbered by people who have often cast doubt on mainstream climate, environmental, and health sciences. For […]
