P-Hacking in Startups: Avoiding Statistical Traps

The pressure to ship fast in startups often leads teams to report any result that looks like an improvement, resulting in p-hacking. This article dissects three common scenarios: multiple comparisons without correction, reframing metrics post-hoc, and running experiments until a hit is achieved. It emphasizes the importance of pre-registering hypotheses and metrics, avoiding post-hoc data dredging, using corrections for multiple comparisons, and applying appropriate thresholds for early peeking. The article advocates for celebrating definitive negative results, arguing that rigorous statistical practices accelerate learning by preventing the release of noise and building a true understanding of user behavior.