Establish a baseline period and track variability before declaring victory. Pair each primary metric with a counter-metric that defends customer experience or unit economics. If experiments improve clicks but worsen retention, your loop should halt expansion and investigate root causes before scaling widely.
When sample sizes are small, complement point estimates with ranges and confidence notes. Treat early results as directional and avoid overfitting decisions to random noise. Batch changes until signals are clearer, and explicitly mark low-confidence bets so you can revisit them deliberately.
Write down how you might be wrong, who benefits if a result looks good, and which metrics you are tempted to cherry-pick. Invite a peer to challenge assumptions monthly. Transparency disarms self-deception and keeps improvement rooted in reality, not wishful thinking.
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