This footnote by Efron appears in the chapter on false discovery rate control:
I am trying to avoid the term "significant" for the rejected cases as dubious terminology even in single-case testing, and worse in the false discovery rate context, preferring instead "interesting". --Bradley Efron "Large Scale Inference" 2010, p.47
What are the arguments pro and con adopting this change in nomenclature? Is it significant that such a prominent statistician is avoiding "significance"?
Biomedicine and healthcare in particular are domains where quantitative evidence is largely based on association studies, whether RCTs or observational. "Links" between therapies and outcomes, or biomarkers and disease states, depend on hypothesis testing framework.
Could adopting "interesting" on a large scale induce reconsideration of the strength of evidence associated with of such linkages, even if p-value or z-scores still quantify the results?