ads-test

To calculate sample size for an A/B test, you need a baseline metric, traffic estimates, and a minimum detectable effect (MDE). This Skill uses those inputs to generate statistical significance calculations and tailored test plans for platforms like Meta and Google Ads.

Setting up a structured marketing experiment requires a defined hypothesis, baseline metric, traffic estimates, and clearly defined success criteria. This framework provides a checklist and platform-specific setup guides to ensure test validity across channels.

Yes, you can plan ad testing campaigns for LinkedIn and TikTok. The framework supports platform-specific test setups, allowing you to configure hypotheses, duration estimates, and success criteria for these specific networks.

Estimating test duration for marketing optimization experiments relies on your baseline traffic and desired statistical confidence level. The framework uses your traffic estimates and sample size calculations to generate an end-to-end duration estimator.

The best way to design an A/B test with statistical significance is to define a 95% confidence level and a specific MDE upfront. This Skill provides a structured hypothesis framework and calculator to scientifically validate your marketing variants.

Ad testing results often lack statistical significance due to insufficient sample size or undefined success criteria. This framework helps you calculate the required sample size and set a proper MDE before launching to ensure valid campaign analysis.