A/B Test Analyzer

Build an A/B test analysis platform that computes statistical significance, effect sizes, and confidence intervals for experiments. The AI interprets results, warns about common pitfalls (peeking, multiple comparisons), and recommends next steps.

0 likes0 forks0 comments

statisticsab-testingexperimentationhypothesis-testing

Objectives

Calculate statistical significance using appropriate tests (z-test, t-test, chi-square)
Display effect sizes and confidence intervals with clear visualizations
Compute required sample sizes for desired statistical power
Use AI to interpret results and flag common experimental pitfalls
Generate shareable experiment reports with methodology documentation

Hints

Use the normal approximation for large-sample proportion tests
Implement sequential analysis methods to handle early peeking correctly
Have the AI check for Simpson's paradox and segment-level differences

Resources

Comments (0)

to leave a comment.

No comments yet. Be the first to comment!