#statistics

Create a statistical analysis tool for A/B test results. Features: 1. Calculate conversion rate for Control vs Variant. 2. Compute p-value using two-proportion z-test. 3. Determine statistical significance at 95% confidence level. 4. Calculate required sample size for desired power (80%). 5. Visualize confidence intervals with error bars. Include interpretation guidelines for non-technical stakeholders.

Create a comprehensive statistical analysis plan. Components: 1. Research questions and hypotheses. 2. Variables (independent, dependent, covariates). 3. Descriptive statistics (means, frequencies, distributions). 4. Data cleaning and missing data handling. 5. Assumption testing (normality, homogeneity). 6. Primary analyses (t-tests, ANOVA, regression). 7. Secondary and exploratory analyses. 8. Multiple comparison corrections. 9. Effect sizes and confidence intervals. 10. Software and packages (R, SPSS, Stata). Pre-register plan before data collection. Avoid p-hacking.

Design statistically valid A/B tests for product features. Pre-test setup: 1. Define hypothesis clearly (adding reviews will increase conversion by 15%). 2. Choose primary metric (conversion rate, not multiple metrics to avoid false positives). 3. Calculate sample size: use online calculators, typically need 1000+ conversions per variant for significance. 4. Set test duration: run for full business cycles (include weekends), minimum 1-2 weeks. 5. Define success/failure criteria upfront. Implementation: 50/50 random split, ensure consistent user experience across sessions. Analysis: statistical significance (p<0.05), confidence intervals, practical significance (is 2% lift worth engineer time?). Avoid peeking at results mid-test. Tools: Optimizely, Google Optimize, VWO, internal feature flags. Document learnings for future tests.