The text explores the complexities and potential pitfalls associated with choosing metrics for experimentation, particularly highlighting issues related to Goodhart's Law, which warns against measures becoming ineffective when they turn into goals. It illustrates how the misuse of metrics can lead to unintended consequences, such as gaming metrics to achieve desired outcomes without improving overall objectives, as seen in historical examples like the Cobra Effect and modern instances such as school rankings manipulation. The text also discusses the importance of distinguishing correlation from causation in metric-driven decision-making, using a case study from a freemium content site that mistakenly equated increased content use with higher revenue, only to find the correlation was non-causal. It emphasizes the necessity of careful metric selection, the risks of proxy metrics, and the need for comprehensive analysis to avoid negative outcomes and improve the effectiveness of experiments.