Free A/B Test Calculator Online for Marketers | CorpoProd

Frequently asked questions

What is minimum detectable effect (MDE)?

MDE is the smallest improvement you want your test to reliably detect. It's expressed as a relative percentage. An MDE of 20% on a 5% baseline conversion rate means you want to detect if the variant achieves 6% or higher. Smaller MDE requires larger sample sizes. Setting MDE too low (e.g., 5%) often requires impractically large samples. Setting it too high (e.g., 50%) means you'll only detect very large improvements. 15-25% is a practical range for most businesses.

Why do I need statistical significance?

Statistical significance tells you the probability that your test result is real rather than due to random chance. At 95% confidence, there's only a 5% chance that a detected difference is a false positive (Type I error). Without statistical significance, you might implement changes based on random variations, potentially hurting conversions. The industry standard is 95% confidence, but 90% is acceptable for low-risk changes and 99% is recommended for changes affecting revenue-critical pages.

Can I stop a test early if results look significant?

No, this is called 'peeking' and dramatically inflates false positive rates. If you check significance daily during a 30-day test, your actual false positive rate can reach 30% instead of the intended 5%. Solutions: pre-commit to the calculated sample size; use sequential testing methods that allow valid interim analysis; or use Bayesian approaches that handle continuous monitoring better than frequentist methods.

How does traffic volume affect test planning?

Traffic determines test duration, not test validity. Whether you have 100 or 100, 000 daily visitors, the required sample size per variant is the same. Low-traffic sites simply need to run tests longer. If your calculator shows a 180-day test, consider: testing higher-impact changes (larger MDE), testing higher-traffic pages, reducing the number of variants, or accepting 90% confidence instead of 95%.

What is statistical power and why does it matter?

Statistical power (typically 80%) is the probability that your test correctly detects a real effect when one exists. 80% power means there's a 20% chance of a false negative (missing a real improvement). For critical tests (major redesigns, pricing changes), use 90% power. The A/B Test Duration Calculator also offers this: proper power analysis using z-test formulas for proportions.

How do I use the A/B test calculator online without signing up?

Enter your baseline conversion rate (your current conversion rate as a percentage). Set the minimum detectable effect (the smallest improvement worth detecting, typically 10-20% relative improvement). Choose your desired confidence level (95% is standard; 99% for critical decisions; 90% for exploratory tests).

What does the free A/B Test Duration Calculator include?

The free A/B Test Duration Calculator includes: proper power analysis using z-test formulas for proportions; configurable confidence levels: 90%, 95%, 99%; configurable statistical power: 80%, 90%, 95%; support for 2, 3, or 4 variant tests (a/b/c/d); daily traffic to duration conversion for practical planning; test parameter summary showing baseline, target, and absolute lift; duration warning for tests exceeding 90 days.

Who is this A/B test calculator for marketers most useful to?

A/B testing is the foundation of data-driven optimization, but poorly planned tests waste time and lead to wrong conclusions. Without calculating the required sample size upfront, teams commonly make two critical errors: stopping tests too early (when they see a promising result that might be random noise) or running tests too long (wasting time on tests that will never reach significance).

What should I check before acting on A/B Test Duration Calculator results?

Always calculate sample size before starting a test, never end a test early because you see a "winner." Set the minimum detectable effect realistically: for most websites, detecting a 5% relative improvement requires unrealistic sample sizes. Focus on detecting 15-25% improvements, which require manageable sample sizes and represent meaningful business impact.

Can the A/B Test Duration Calculator help with statistical significance calculator?

Our free calculator uses proper power analysis formulas to compute the required sample size per variant based on your baseline conversion rate, minimum detectable effect (MDE), desired confidence level, and statistical power. It then factors in your daily traffic volume to estimate the exact number of days needed.