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statistical-significance

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Analyzes the results of A/B tests to determine if there is a statistically significant difference between control and treatment groups. It provides a structured approach for performing A/B tests, interpreting results, and making data-informed decisions. A valuable resource for marketers and product managers aiming to optimize user experience.

  • Updated Oct 1, 2024
  • Python

This repository contains a detailed case study on an A/B test of LunarTech's homepage CTA button, using proxy data structured similarly to the company's real data.

  • Updated Feb 5, 2025
  • Jupyter Notebook

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