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statistical-significance

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Benchmark 9 retrieval architectures (vector, contextual, QnA, knowledge graph, hybrid, RAPTOR, PageIndex, BM25, rerank) on your own docs. Automated hyperparameter search with bootstrap CIs and significance tests.

  • Updated May 21, 2026
  • Python

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

Revenue optimization project for an online store. Applied ICE/RICE frameworks for hypothesis prioritization and conducted a rigorous A/B test analysis. Managed statistical significance, data filtering (outliers), and conversion rate modeling to drive data-led business decisions.

  • Updated Jan 6, 2026
  • Jupyter Notebook

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