This whitepaper addresses common pitfalls in experimentation that cause teams to draw incorrect conclusions from A/B tests. It explains how issues like small sample sizes, selection bias, seasonality, and metric misalignment can distort results and lead to false confidence. The guide emphasizes designing experiments with clear hypotheses, choosing leading and lagging metrics carefully, and validating statistical significance before acting. It also highlights the importance of context—understanding user segments, external variables, and unintended side effects. The paper positions experimentation as a discipline requiring rigor, transparency, and collaboration between analysts and business teams to drive reliable, repeatable learning.

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