Usually, people with such taxonomies will also think that strong evidence by default trumps weak evidence, allowing you to entirely ignore it. That is not how that works. Either something has a likelihood ratio, or it doesn’t.
This is wrong, strong evidence does typically trump weak evidence, allowing you to ignore the weak evidence for the purpose of the question you are trying to answer.
Specifically, the way you get strong and weak evidence is when the strong evidence is fairly directly related to the question that you are interested in, while the weak evidence is also dependent on many other contingent and random factors. In such a circumstance, strong evidence gets rid of your uncertainty about the question under consideration, which leaves the weak evidence only correlated with the random contingent factors and not with the question itself.
So in the presence of strong evidence, weak evidence shifts from being evidence about the fact of the matter to being evidence about its own quality. If the weak evidence contradicts the strong evidence then it’s probably due to some random contingent factor like sample bias or whatever, and you can (approximately) ignore it.
This is wrong, strong evidence does typically trump weak evidence, allowing you to ignore the weak evidence for the purpose of the question you are trying to answer.
Specifically, the way you get strong and weak evidence is when the strong evidence is fairly directly related to the question that you are interested in, while the weak evidence is also dependent on many other contingent and random factors. In such a circumstance, strong evidence gets rid of your uncertainty about the question under consideration, which leaves the weak evidence only correlated with the random contingent factors and not with the question itself.
So in the presence of strong evidence, weak evidence shifts from being evidence about the fact of the matter to being evidence about its own quality. If the weak evidence contradicts the strong evidence then it’s probably due to some random contingent factor like sample bias or whatever, and you can (approximately) ignore it.