Execute tests with proper controls. Avoid peeking at results early. Monitor for external factors that could invalidate your test.
You can't run every experiment at once. Limited time, budget, and traffic force trade-offs. Prioritisation frameworks like ICE (Impact, Confidence, Ease) and PIE (Potential, Importance, Ease) score experiments objectively so you're not guessing. This removes politics and gut feel from the decision. The highest-scoring experiments get resourced first. This chapter shows you how to score experiments consistently and build a prioritised testing queue.
Determine the winner with statistical confidence. Document what you learned. Roll out winning variants and feed insights into the next test.
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Flexible workspace for docs, wikis, and lightweight databases ideal when you need custom systems without heavy project management overhead.
Compare two versions of a page, email, or feature to determine which performs better using statistical methods that isolate the impact of specific changes.
Structure experiments around clear predictions to focus efforts on learning rather than random changes and make results easier to interpret afterward.
Systematically rank projects and opportunities using objective frameworks, ensuring scarce resources flow to highest-impact work.
Identify and leverage limitations as forcing functions that drive creative problem-solving and strategic focus.
Execute tests with proper controls. Avoid peeking at results early. Monitor for external factors that could invalidate your test.
