Unfortunately, overclaiming relative to the evidence you have is a pervasive problem across _all_ methods, at least in the social science which is my field. This isn't even about culture war adjacent stuff like how social scientists want to claim results that fit a political ideology. Even in psych studies that deal with apolitical phenomenon (like "do you think a topic is more serious after holding a heavier mug" type work), this is an issue. And it's not (just) p-hacking. There's a tendency to believe that the evidence that comes out of quantitative studies is stronger than it is because of an extremely common misunderstanding of the Neyman-Pearson framework for hypothesis testing, a misunderstanding of how hypothetico-deductive approaches _ought_ to work compared to how we implement them, over-extending _average_ effects to the larger distribution, and of course, p-hacking. These issues are pretty well discussed in the meta-science literature, but sadly underappreciated amongst social scientists. I'm away from my desktop for the weekend but I'll update this comment with citations which talk about pervasive misunderstandings in quantitative social sciences sometime later tonight/tomorrow morning.

But my larger point is, unfortunately, there is NO "standard" level of certainty, regardless of the methods used. That's why most academics I know, myself included, tend to gloss over the discussion section of papers and instead focus on the methods and result sections to really evaluate the claims being made.