Read past a 99%-accurate screen into its four confusion-matrix cells, slide the threshold to trade false alarms against missed cases, and set the cut-off by which mistake costs most.

A screening model runs `return "healthy"` on all 1,000 patients, misses the single sick one, and still prints 99.9% on its report card. On paper it is the best model in the building, in the clinic it is worthless.

Re-shuffle a single train/test split and watch two close models trade first place, then rotate the held-out slice with cross-validation until the ranking holds, break ties toward the simpler model, and seal the test set so tuning never inflates the verdict.

Two spam filters score 98% and 97%, so you ship the first, then flip one line, `random_state=3`, re-shuffle the same rows, and now the 97% model leads. Nothing about either model changed.

Skew a hiring screener's training pile and watch its decisions tilt with no biased rule in sight, see why piling on more of the same data deepens the slant instead of fixing it, and carry two questions into every confident prediction: was the data representative, and is this a case the model has really seen.

Feed a hiring model ten years of past resumes with `model.fit(past_hires)` and let it decide who advances. It copies the pattern in that pile faithfully, including the one where nearly everyone hired before was a man.