Wrap-up: A/B testing in python

A/B Testing in Python

Moe Lotfy, PhD

Principal Data Science Manager

A/B testing summary

Chapter 1

  • A/B testing steps and use-cases
  • Metrics definition and estimation
  • .sample(), .corr(), pairplot, heatmap

Chapter 2

  • Formulating A/B testing hypotheses
  • Error rates, power, effect size
  • Power analysis: sample size estimation
  • Multiple comparisons corrections

Chapter 3

  • Data cleaning and EDA
  • Sanity checks for validation
  • Analyzing difference in proportions
  • proportions_ztest,proportion_confint

Chapter 4

  • Analyzing differences in means
  • Non-parametric tests
  • Delta method for ratio metrics
  • Best practices and advanced topics
A/B Testing in Python

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A/B Testing in Python

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