Work through how confounding can create a misleading causal story from observational business data.
A report shows that stores with higher staffing hours also report higher weekly sales, and leaders want to translate that relationship straight into a labor-allocation decision. In observational data, a variable can correlate with an outcome because it causes it, because the outcome influences it, or because a third factor drives both. Common trap: accept an intuitively plausible association as causal without checking whether demand, store size, or traffic explains both sides of the relationship. Step 1 Describe the observed pattern accurately: higher-staffed stores also show higher sales. This descriptive statement is legitimate. The problem starts only when the team…
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