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A Distinguished Speaker Series Seminar by John P. A. Ioannidis, M.D., D.Sc.

For years, scientists have expressed concern about the lack of reproducibility in research findings—concerns that span all phases of scientific inquiry: basic, translational, applied, and implementation. Alongside these issues, doubts have emerged about the practical usefulness of many late-stage research efforts that, ideally, should have delivered tangible value but often fall short. The result is a substantial amount of avoidable waste in the scientific enterprise.

Over the past 15 years, these concerns have increasingly been framed as part of a broader “replication crisis.” Large-scale efforts to assess replicability across diverse disciplines have confirmed that reproducing scientific results is far from straightforward. Empirical and theoretical investigations have also revealed numerous forms of bias, some of which are more prevalent in certain fields than others. While awareness of these problems has grown, proposed solutions vary widely—ranging from speculative theoretical fixes to empirically grounded interventions. Yet many of these proposals lack rigorous evaluation, and some may inadvertently intensify the very issues they aim to resolve.

In this seminar, Professor John P. A. Ioannidis will explore the factors that undermine reproducibility and research utility, including how studies are conducted, reported, and reviewed. He will also assess the effectiveness and desirability of proposed interventions, highlighting what has been tested, what remains speculative, and how current developments in meta-research can inform a more reliable scientific process. Importantly, he will apply a critical lens not only to individual studies but also to the field of meta-science itself, examining the progress—and limitations—of this emerging discipline.

This seminar is ideal for researchers who want to better understand the structural and methodological sources of irreproducibility, and who are seeking practical, evidence-informed approaches to improving the trustworthiness and value of scientific research.

Prerequisites: Open to researchers from any scientific field with a basic understanding of statistics.