science A/B Testing
query_stats Statistics
attach_money Conversion
A/B Testing & Hypothesis Validation
A/B tests to assess conversion improvements: data cleaning, hypothesis definition, Welch t-test, and assumption validation for evidence-backed decisions.
Stack
Python
Statistics
A/B Testing
flag
Objective
Validate whether a new checkout flow improves conversion compared to control, maintaining statistical rigor.
account_tree
Methodology
- Preparation: clean visitor and order data, verify the A/B balance.
- Hypotheses: H0 assumes no difference; H1 expects conversion B > A.
- Testing: conversion rate, Welch t-test (p<0.05), and assumption checks (Shapiro, Levene).
- Visualization: histograms and boxplots of conversion by group.
bar_chart
Key visualizations
- • Conversion distribution by group A vs. B.
- • Daily trend of the conversion rate throughout the experiment.
- • Summary of p-values and confidence intervals.
emoji_events
Results
- • Significant difference favoring group B (p<0.05).
- • Recommendation to deploy variant B and monitor for two more weeks.
- • Reusable template for future A/B experiments.