The upper range of a collected dataset is most likely accurate, but the rest may suffer biased omissions: How to call this phenomenon? Background: In collecting a dataset of a specific unit ordered by a numeric variable, it is possible that the upper 'cloud' of the dataset is correct, while the 'tail' seems inaccurate.
I can thus trust the upper bounds of the dataset, while I deem the mid- and lower ranges to be rather questionable.
Question: Is there a data scientific term for this phenomenon? If so, how is it called?
Example: I use various sources to gather a list of major publishers of scholarly journals. In total, I found 150 publishers that publish at least 50 journals.
At the top, I found the publisher AAA with 3.000 journals, BBB with 2.500 journals, CCC with 1.900 journals, DDD with 1.500 journals etc.
With my industry knowledge,  I can confirm that the top 20 is likely to be accurate.
However, in the lower ranges, there are publishers like XXX with 51 journals, YYY with 50 journals, ZZZ with 50 journals etc. Many of them are rather obscure, and even as an expert I may have never heard of them. I can imagine that the 'tail' is rather inaccurate with large-scale omissions, such as publishers from the Global South.
I thus tend to trust the top 20 or so of that list, but not the ones that rank between #21 and #150.
The case described can be cast as imbalanced dataset problem, or rare events problem.
Even more generally as highly non-uniform underlying distribution problem (which is an umbrella term for both cases).
References:

Machine Learning Tips: Handling Imbalanced Datasets
Handling imbalanced datasets in machine learning