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An A/B test you can actually trust
Run an experiment and analyse it without fooling yourself.
Why it matters
Most reported A/B wins are noise. Being the person who asks about sample size and peeking is immediately valuable to any product team.
What you build
- An experiment design with a pre-computed sample size
- Assignment that is stable and unbiased
- Significance testing done correctly
- A written result including 'no effect'
If you want more
- Add sequential testing
- Add segment-level analysis
Build it
Deciding after you see the data is how you get a result you like.
- Assign users randomly and stably
Material for the whole build
Shows statistical literacy applied to a product decision, which is rarer than it should be.
Get a plan built around projects like this