Stopping Early in Product Experiments Without P-Hacking Using mSPRT
product experimentation•Jul 2, 2026

Stopping Early in Product Experiments Without P-Hacking Using mSPRT

Discover how to avoid p-hacking and stop early in product experiments using the mixture Sequential Probability Ratio Test (mSPRT) and sequential testing. This tutorial provides a step-by-step guide on implementing mSPRT in Python and applying it to a synthetic LLM product dataset.

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Key summary

Learn how to stop early in product experiments without p-hacking using the mixture Sequential Probability Ratio Test (mSPRT) and sequential testing in Python.

TL;DR

  • Optional stopping breaks classical tests due to inflated false positive rates.
  • mSPRT provides always-valid inference using an e-value.
  • Sequential testing is designed for live streams with multiple checks.
  • Python implementation of mSPRT is provided in the tutorial.
  • mSPRT fails when assumptions are not met, such as identification and prerequisites.
  • Bootstrap confidence intervals can be used to validate results.
  • Always-valid sequential testing frameworks are used in production at Netflix.
#product experimentation
#experimentation
#causal inference
#ai
#machine learning

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