TikTok A/B Testing Suite
ABOUT THIS PROJECT
Simulates a real-world A/B experimentation suite across 4 concurrent TikTok product tests — with rigorous statistical analysis and an executive-grade Streamlit dashboard.
Covers Welch's T-Test for continuous metrics, Chi-Square for conversion rates, Cohen's d for effect sizes, and power analysis for sample sizing. The Streamlit dashboard is designed to read like an internal TikTok product review doc — black header, gold accents, Playfair Display, and green/red verdict cards for instant decision clarity.
WHAT I LEARNED
Welch's T-Test over Student's T-Test is almost always the right default — assuming equal variance between control and treatment groups is rarely justified in real product experiments
Cohen's d made effect sizes legible to non-statisticians — a p-value alone doesn't tell you if a result is worth shipping, but 'medium effect' does
Running 4 concurrent tests exposed the multiple comparisons problem — Bonferroni correction feels conservative but it's the honest approach when testing several hypotheses at once
Dashboard design for stakeholders is a different skill from analysis — the verdict cards weren't aesthetic choices, they were communication choices
Power analysis before running a test is the step most people skip — it's the only way to know if your sample size is actually large enough to detect the effect you care about
TECH STACK
LIVE PREVIEW
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