Science Is Still Persuasion (hunchbox.substack.com)

A recent discussion highlights the transformative impact of AI on scientific research, particularly in empirical social sciences. AI accelerates the process from inquiry to findings, allowing researchers to collect more data and explore questions multiple times in shorter timeframes. This efficiency poses important questions about scientific rigor and the uncertainties involved in research, particularly around what is termed "researcher degrees of freedom." These degrees represent the myriad choices researchers make that can significantly alter outcomes—potentially reaching into the thousands of variations. Understanding and reporting these variations, or "non-standard errors," can enhance the credibility of scientific claims. As AI tools become integral to the research process, the expectation is that authors will need to report the range of estimates influenced by their methodological decisions alongside traditional standard errors. This shift towards transparency may lead to a new paradigm where scientific papers include platforms for readers to explore alternative scenarios. Such an evolution would not only enhance reproducibility but also move scientific publishing towards a more dynamic, interactive model. Journals are well-positioned to facilitate these changes by standardizing data sharing and promoting "living papers" that adapt to new inquiries, ultimately advancing the pursuit of reliable scientific knowledge.