Discussion questions#
When does data become knowledge? Suppose a model predicts behavior or neural activity with high accuracy (i.e., minimal error).
Under what conditions would you feel comfortable saying it has produced knowledge rather than just prediction?
Is high accuracy enough, or do we need causal understanding?
How does this distinction matter for scientific claims vs. practical applications?
What gets lost when we turn experience into variables? Most data science requires converting rich phenomena into variables.
What kinds of information are preserved by this move?
What kinds of information might be lost or distorted?
How do these losses matter for interpretation and theory-building?