Explanation

This scenario describes data snooping, which occurs when:

Key Characteristics:

• Multiple strategies/models are tested
• Selection is based purely on statistical significance (lowest p-value)
• No consideration of economic rationale or prior theory
• High risk of finding spurious relationships due to multiple testing

Why this is data snooping:

1. Multiple testing problem: Testing 5 factors creates multiple opportunities to find statistically significant results by chance
2. P-hacking: Selecting based on lowest p-value without theoretical justification
3. Overfitting: The strategy may work well in-sample but fail out-of-sample

Why not other biases:

• Survivorship bias: Not applicable - this involves using only surviving entities in analysis
• Look-ahead bias: Not applicable - the analyst used point-in-time data, which avoids this bias

Best Practice:

• Use economic theory to guide factor selection
• Apply out-of-sample testing
• Use proper multiple testing corrections
• Consider economic significance, not just statistical significance

Conclusion: This approach represents classic data snooping behavior.