The Cost of 'Waiting for the Data': Why Curiosity Without Guardrails Undermines Research
In fields like microbial ecology, agriculture, and molecular biology, the practice of 'waiting to see what the data say' is often justified as embracing discovery. But without rigorous upfront design—particularly computational modeling to guide measurement strategies and statistical approaches—this flexibility masks p-hacking and arbitrary choices. This post argues that modeling, preregistration, and honest distinction between exploratory and confirmatory work aren't constraints on discovery; they're prerequisites for credible science. Targeted at computational professionals and data scientists, it offers practical guidance for designing rigorous studies, collaborating with wet-lab teams, and building institutional cultures around methodological transparency.
Arbitrary experimental choices disguised as data-driven discovery lead to p-hacking, waste, and irreproducible results. How modeling and preregistration protect scientific integrity in computational biology, microbial ecology, and agriculture.