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Ice cream and drownings: correlation mistaken for cause

Gelati e annegamenti: correlazione scambiata per causa · Data Analysis · Beginner

The case

The marketing director shows you a chart: in months where email spend rises, sales rise too, correlation of 0.85. He concludes doubling email budget doubles sales and wants to triple it now. The chart is convincing and the correlation strong. Before he signs the budget increase, how do you explain you might be seeing a correlation that doesn't imply causation?

Il direttore marketing ti mostra un grafico: nei mesi in cui la spesa in email marketing sale, salgono anche le vendite, con correlazione dello 0,85. Conclude che raddoppiando il budget email si raddoppiano le vendite e vuole triplicarlo subito. Il grafico è convincente e la correlazione forte. Prima che firmi l'aumento di budget, come spieghi che potresti star vedendo una correlazione che non implica causa?

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Frequently asked questions

What is the "Ice cream and drownings: correlation mistaken for cause" problem-solving case on Grace?

It is a beginner-level Data Analysis case based on a real workplace situation. You read the problem, write how you would solve it in free text, and Grace's AI judges whether your solution actually works — or what is still uncovered.

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