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During the last decade, the information technology industry has adopted adata-driven culture, relying on online metrics to measure and monitor businessperformance. Under the setting of big data, the majority of such metricsapproximately follow normal distributions, opening up potential opportunitiesto model them directly without extra model assumptions and solve big dataproblems via closed-form formulas using distributed algorithms at a fraction ofthe cost of simulation-based procedures like bootstrap. However, certainattributes of the metrics, such as their corresponding data generatingprocesses and aggregation levels, pose numerous challenges for constructingtrustworthy estimation and inference procedures. Motivated by four real-lifeexamples in metric development and analytics for large-scale A/B testing, weprovide a practical guide to applying the Delta method, one of the mostimportant tools from the classic statistics literature, to address theaforementioned challenges. We emphasize the central role of the Delta method inmetric analytics by highlighting both its classic and novel applications.

Applying the Delta method in metric analytics: A practical guide with novel ideas