In the case of flossing’s benefits, the supposedly weak evidence cited by The Associated Press was the absence of support in the form of definitive randomized controlled trials, the so-called gold standard for scientific research. Why was there so little of this support? Because the kind of long-term randomized controlled trial needed to properly evaluate flossing is hardly, if ever, conducted — because such studies are hard to implement. For one thing, it’s unlikely that an Institutional Review Board would approve as ethical a trial in which, for example, people don’t floss for three years. It’s considered unethical to run randomized controlled trials without genuine uncertainty among experts regarding what works.

And dentists know from a range of evidence, including clinical experience, that interdental cleaning is critical to oral health and that flossing, properly done, works. Yet the notion has taken hold that such expertise is fatally subjective and that only randomized controlled trials provide real knowledge.

The opposition between randomized controlled trials and expert opinion was fueled by the rise in the 1990s of the evidence-based medicine movement, which placed such trials atop a hierarchy of scientific methods, with expert opinion situated at the bottom. The doctor David Sackett, a father of the movement, once wrote that “progress towards the truth is impaired in the presence of an expert.”

But while all doctors agree about the importance of gauging the quality of evidence, many feel that a hierarchy of methods is simplistic. As the doctor Mark Tonelli has argued, distinct forms of knowledge can’t be judged by the same standards: what a patient prefers on the basis of personal experience; what a doctor thinks on the basis of clinical experience; and what clinical research has discovered — each of these is valuable in its own way. While scientists concur that randomized trials are ideal for evaluating the average effects of treatments, such precision isn’t necessary when the benefits are obvious or clear from other data.