• [x] Статистика и АБ-тестирование @ Wiki
• [ ] Analyzing Experiment Outcomes: Beyond Average Treatment Effects | Uber Engineering Blog
• [ ] Simple Sequential A/B Testing – Evan Miller
• [ ] A/B Testing Rigorously (without losing your job) | Hacker News
• [ ] Bayesian A/B test code
• [ ] Agile A/B testing with Bayesian Statistics and Python
• [ ] Why use a Bayesian approach to A/B testing? - Swrve Help Center
• [ ] Is Bayesian A/B Testing Immune to Peeking? Not Exactly – Variance Explained
• [ ] Doing Bayesian Data Analysis: Optional stopping in data collection: p values, Bayes factors, credible intervals, precision
• [ ] Rapid A/B-testing with Sequential Analysis | Audun M Øygard
• [x] A/B Testing Rigorously (without losing your job)
• [ ] Easy Evaluation of Decision Rules in Bayesian A/B testing - Chris Stucchio
• [ ] The Power of Bayesian A/B Testing – Convoy Tech – Medium
• [ ] Understanding Bayesian A/B testing (using baseball statistics) – Variance Explained
• [x] 045. Как измерить счастье пользователя — Илья Кацев - YouTube
• [x] Delta method - Wikipedia
• [ ] Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix
• [ ] How Booking.com increases the power of online experiments with CUPED
• [ ] p_hacking.pdf
• [ ] wsdm55-deng.dvi
• [ ] A/B Materials for In-Depth Study · martsen
• [x] An introduction to the Poisson bootstrap
• [ ] How to Deal With Ratio Metrics When Accounting for Intra-User Correlation in A/B Testing
• [ ] Applying the Delta Method in Metric Analytics: A Practical Guide with Novel Ideas
• [ ] Uncertainty in Online Experiments with Dependent Data:
• [x] Bootstrap with weights
• [ ] Continuous Monitoring of A/B Tests without Pain: Optional Stopping in Bayesian Testing
• [ ] FAIRNESS THROUGH EXPERIMENTATION: INEQUALITY IN A/B TESTING AS AN APPROACH TO RESPONSIBLE DESIGN
• [ ] si-notes.pdf
• [ ] What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statisticsat Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics
• [ ] Split-door criterion: Identification of causal effects through auxiliary outcomes
• [ ] Detecting Network Effects: Randomizing Over Randomized Experiments
• [ ] Interactive identification of individuals with positive treatment effect while controlling false discoveries
• [ ] Using Sentiment Score to Assess Customer Service Quality | by Shuai Shao (Shawn) | The Airbnb Tech Blog | Jul, 2021 | Medium
• [ ] Experiments at Airbnb | by AirbnbEng | The Airbnb Tech Blog | Medium
• [ ] Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects
• [ ] Trustworthy Online Marketplace Experimentation with Budget-split Design