Statistical significance describes how strongly observed data challenge a specified null hypothesis within a statistical testing procedure. It is not the same as practical or business importance: a relationship may be statistically significant without being meaningful for a decision. section 3.2
Interpret statistical significance alongside expected result patterns, correlation size, predictive power, and domain knowledge. Generative AI can assist with analytical design, but it cannot replace judgment about whether an insight is valuable or misleading. section 3.2
Use statistical significance cautiously by:
- Separating evidence against a null hypothesis from the practical meaning of a result. section 3.2
- Checking whether the observed result fits expected patterns before treating an unexpected result as relevant. section 3.2
- Combining statistical testing with business understanding rather than accepting an AI-proposed method automatically. section 3.2
