Prioritize experiment ideas by balancing how much evidence supports them and how expensive they are to implement.
Implementation cost Low High Evidence strength Run next High evidence · Low cost Best candidates for the next slot because the hypothesis is already supported and the setup is lightweight. Prepare carefully High evidence · High cost Worth doing, but only when the expected learning justifies the engineering and operational overhead. Sharpen first Low evidence · Low cost Good for quick discovery or lightweight validation before you commit meaningful sample and team attention. Do not queue yet Low evidence · High cost Weakest next bets because they consume capacity before the hypothesis has earned it.
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