A statistically visible change does not answer the decision alone
A fictional project compares an operational metric before and after a pilot. The supplied analysis finds a small statistically significant difference, but it also records implementation cost, additional review work and uncertain representativeness of the pilot users. A colleague proposes approving a company-wide rollout based only on the p-value. Another asks for effect size, practical outcome, uncertainty, design limitations and an accountable decision under the actual business objective. This written case approves no spending or rollout. It asks the learner to distinguish statistical evidence from a complete value/risk judgment and explain why a transparent recommendation should not hide relevant costs or untested generalization assumptions.
1. What does the p-value not establish?
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It does not alone establish practical importance, causation, worthwhile net value or applicability to the whole company. Those require the relevant design, effect and cost evidence.
2. What should the recommendation include?
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Present the bounded observed effect and uncertainty, quality and rework costs, comparison assumptions, user scope, risks and justified next steps for the authorized decision owner.