CS Professional · Artificial Intelligence, Data Analytics and Cyber Security - Laws and Practice · Data Analytics
Kaveri Textiles Ltd. analyses customer purchase data and finds that two unrelated variables, ice-cream sales and its dealer complaints, rise together in summer. A manager proposes cutting ice-cream promotions to reduce complaints. What is the analytical error?
The error is confusing correlation with causation. Both variables rise in summer because of a common seasonal factor, not because one drives the other, so cutting promotions would not be expected to reduce dealer complaints.
- AConfusing correlation with causationCorrect
- BUsing descriptive analytics instead of big data
- COverfitting due to too few variables being excluded
- DIgnoring data encryption requirements
Explanation
Two variables moving together, here likely because of the common seasonal factor, does not mean one causes the other. Acting on the correlation as if it were causal is the flaw. Encryption, overfitting and the descriptive versus big data distinction are not the issue.
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