How would you measure a sales forecast model's accuracy?
Tests if you can link statistical metrics to business outcomes. Define MAE (average error) and RMSE (penalizes large errors). Choose RMSE when large misses are costly (e.g., stock-outs), MAE otherwise. A red flag is reciting formulas without business context.
This tests your ability to connect abstract metrics like MAE and RMSE to concrete business outcomes, not just recite formulas. A strong answer defines MAE as the average error and RMSE as penalizing large errors due to squaring. You'd prefer RMSE when large forecast misses are disproportionately costly (e.g., stock-outs) and MAE when the business cost of errors is linear. The common mistake is failing to explain *why* the squaring in RMSE matters from a business perspective.
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