Every company that gets serious about experimentation passes through a phase where they test, get an answer they don't like, and find a reason to ignore it. Here's how to recognize it and move past it.
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AI-accelerated code production increases the need for experimentation. The validation bottleneck grows with build speed. The fastest learners will win.
Most sample size calculators assume a fixed-sample test with a single metric. If your experiment uses sequential testing, corrects for multiple metrics, or applies variance reduction, the number is inaccurate and you are wasting resources.
AI made building cheap. It also made bad decisions cheaper to ship. The distance between execution speed and validation speed is the judgment gap.
Most teams justify experimentation by counting winners. The real value shows up in three places: shipped wins, prevented harm, and how fast the organization learns from results.
The A/B testing curriculum Spotify built over ten years to train thousands of experimenters is now free and open to everyone.
Powering an experiment does not make its results trustworthy. Trustworthiness depends on whether the effect you powered for matches the true effect, and that is rarely something you can know in advance.
Bonferroni's conservatism reputation is mostly a denominator mistake. Here is why it holds up once you correct only the metrics that need it.
Learn how wrong things can go when proxy metrics start to influence product development, and how to use them safely.
Why we maintain distinct technology stacks for personalization and experimentation.
