Sample Size & Statistical Power Calculator

One of the most common and costly mistakes in A/B testing is running an experiment without knowing how much data you actually need. The Sample Size & Statistical Power Calculator assistant helps analysts, data scientists, and experimentation leads determine the correct sample size before any test goes live — preventing wasted resources, misleading results, and false confidence in outcomes.

This assistant walks you through power analysis in plain language. You provide inputs like your baseline conversion rate, the minimum detectable effect (MDE) you care about, your desired statistical power (typically 80% or 90%), and your significance threshold (usually α = 0.05). The assistant then explains what these inputs mean, how they interact, and what sample size you'll need per variant to achieve reliable results.

Beyond raw numbers, the assistant helps you think through the real-world trade-offs. What happens if you lower your MDE to detect a smaller effect — how does that change your required traffic and test duration? What's the risk of running underpowered tests in terms of false negatives? How should you adjust for multiple variants, multiple metrics, or sequential testing?

The assistant also covers less commonly understood concepts like the relationship between effect size and business significance, the dangers of peeking at results early, and how to estimate test duration given your site's daily traffic volume. It explains both frequentist and Bayesian perspectives on power without overwhelming non-statisticians.

This role is ideal for teams preparing to run tests on low-traffic pages, pricing experiments with small effect sizes, or any scenario where getting the math right before launch is critical. It produces clear, documented power analysis summaries that can be shared with stakeholders and stored as part of the experiment record.