Hassan Ijaz
Ai, Web & Design
A/B testing simulator
Website optimization game where users run tests, see results accumulate, and learn about statistical significance
Concept Overview
A/B testing is a controlled experiment comparing two versions to determine which performs better. It's widely used in product development, marketing, and user experience optimization.
Experimental Design
Random Assignment
- Users randomly assigned to control (A) or treatment (B)
- Eliminates selection bias and confounding
- Ensures groups are comparable on average
- Foundation for causal inference
Key Metrics
- Primary metric: Main outcome of interest
- Secondary metrics: Additional insights
- Guardrail metrics: Ensure no negative side effects
- Choose metrics before running experiment
Statistical Analysis
Two-Sample Tests
- Proportions: Z-test or χ² test
- Means: Two-sample t-test
- Non-parametric: Mann-Whitney U test
Effect Size
- Absolute difference: |p_B - p_A|
- Relative lift: (p_B - p_A) / p_A
- Practical significance vs statistical significance
Sample Size & Duration
Power analysis determines required sample size:
- Minimum detectable effect (MDE)
- Statistical power (typically 80%)
- Significance level (typically 5%)
- Baseline conversion rate
Run until reaching planned sample size, not until significance!
Common Pitfalls
Peeking Problem
Stopping early when results look significant inflates Type I error
Multiple Comparisons
Testing many metrics without correction increases false positives
Novelty Effects
Users may behave differently initially, effects may not persist
Simpson's Paradox
Aggregate results may reverse when segmented by subgroups
Advanced Techniques
Sequential Testing
Continuous monitoring with error control
Bayesian A/B Testing
Probability statements about treatment effects
Multi-Armed Bandits
Adaptive allocation based on interim results
Stratification
Control for known covariates to reduce variance
Best Practice: Focus on business impact, not just statistical significance. A statistically significant 0.1% improvement might not justify implementation costs.
The website optimization game below lets you run A/B tests and see results accumulate. Learn about statistical significance while optimizing conversion rates!
Interactive Visualization
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Website optimization game where users run tests, see results accumulate, and learn about statistical significance