L1 and L2 in the diagram have smaller slopes than B1 and B2, and yet their sum is higher. It's not hard to characterize when this happens. So the canonic example is that a drug might be more effective against all sub-cases (e.g. mild, severe illness) and yet appear less effective overall.
You, on the other hand, seem to be describing selection bias.
Simpson's paradox can be thought of as selection bias as well. To make the Simpson's paradox clear, consider the following simplified scenario: autopilot has more accidents per city mile, and more accidents per highway mile, but because highway miles have fewer accidents, and autopilot is tested on a much higher proportion of highway miles, on average it has fewer accidents per mile overall.
Simpson's paradox is easier to understand geometrically.
https://en.m.wikipedia.org/wiki/File:Simpson_paradox_vectors...
L1 and L2 in the diagram have smaller slopes than B1 and B2, and yet their sum is higher. It's not hard to characterize when this happens. So the canonic example is that a drug might be more effective against all sub-cases (e.g. mild, severe illness) and yet appear less effective overall.
You, on the other hand, seem to be describing selection bias.