Why Data Quality Wins

The industry cliché is 'just get more data'. But a million mislabeled examples teach a million wrong lessons. Audits of famous benchmark datasets have found ~6% of labels are simply wrong — and models trained on them learn the errors faithfully.

Three quality killers to watch for:

• Label errors — wrong answers baked into training data
• Duplicates — the same example counted twice, inflating confidence
• Leakage — test information sneaking into training, making scores meaningless