Not about randomness - but about curve fitting. It is actually very difficult to verify non-linear effects -- or maybe I should say the opposite. The statistical tools we use to identify non-linearities are prone to be very noisy, so even in the subset of data including the no-variation responses I am quite skeptical that downward increase is real, or just due to variance in the tails of the data.
So a common social science finding is `* graded effects` where you might not by default expect them, and that is the main headline of the paper. I think plateau effects are reasonable in many situations, https://blogs.sas.com/content/iml/2020/12/14/segmented-regre..., but the noisy data itself often won't be able to clearly differentiate between different curve shapes.
I'm surprised to see this comment not echoed by more people. Besides the inadequate sample size of older participants, my immediate response to the variance in the data was that I doubt the slope could be distinguished from zero.
So a common social science finding is `* graded effects` where you might not by default expect them, and that is the main headline of the paper. I think plateau effects are reasonable in many situations, https://blogs.sas.com/content/iml/2020/12/14/segmented-regre..., but the noisy data itself often won't be able to clearly differentiate between different curve shapes.