Ads Test

Builds structured A/B test plans for paid ads with proper statistical rigor. Takes your test idea and walks you through hypothesis framing, sample size calculations, duration estimates, and platform-specific setup for Meta, Google, LinkedIn, and TikTok. The hypothesis framework alone is worth it: forces you to state the variable, expected impact size, and reasoning before you spend a dollar. Includes lookup tables for required sample sizes at different conversion rates and minimum detectable effects, which beats guessing when to call a test. Honestly most useful for preventing the classic mistakes like ending tests early or changing multiple variables at once. If you run paid experiments beyond just launching stuff and hoping, this gives you the structure to do it properly.