The fair-goers, having knowledge of oxen, had no bias in their guesses
[EDIT: I read this as “having no knowledge of oxen” instead of “having knowledge of oxen”—is this what you meant? The comment seems relevant nevertheless.]
This does not follow: It is entirely possible that the fair-goers had no specific domain knowledge of oxen, while still having biases arising from domain-general reasoning. And indeed, they probably knew something about oxen—from Jaynes’ Probablity Theory:
The absurdity of the conclusion [that polling billion people tells the height of China’s emperor with accuracy 0.03 mm] tells us rather forcefully that the √N rule is not always valid, even when the separate data values are causally independent; it is essential that they be logically independent. In this case, we know that the vast majority of the inhabitants of China have never seen the Emperor; yet they have been discussing the Emperor among themselves, and some kind of mental image of him has evolved as folklore. Then, knowledge of the answer given by one does tell us something about the answer likely to be given by another, so they are not logically independent. Indeed, folklore has almost surely generated a systematic error, which survives the averaging; thus the above estimate would tell us something about the folklore, but almost nothing about the Emperor.
[EDIT: I read this as “having no knowledge of oxen” instead of “having knowledge of oxen”—is this what you meant? The comment seems relevant nevertheless.]
This does not follow: It is entirely possible that the fair-goers had no specific domain knowledge of oxen, while still having biases arising from domain-general reasoning. And indeed, they probably knew something about oxen—from Jaynes’ Probablity Theory: