Mobile A/B Test
Calculator
Plan and analyze experiments for your mobile app. Find out how many users you need, how long to run the test, and whether your results are statistically significant.
๐งช What is A/B Testing?
A/B testing compares two versions of a screen or feature to see which performs better. You show version A to half your users and version B to the other half, then measure which converts more users. This calculator helps you run statistically valid experiments.
๐ Sample Size Calculator
Before running an experiment, calculate how many users you need to get reliable results.
Your current success rate before the test
e.g., 10 = detect a 10% improvement
Chance of detecting a real effect
How sure you want to be
Need X sessions? Track them without compromising privacy.
See Privacy-First Tracking โFrequently Asked Questions
How do I calculate sample size for mobile A/B tests?
Use our Sample Size Calculator. Enter your baseline conversion rate, minimum detectable effect (MDE), and desired statistical power. The calculator uses a two-proportion power analysis formula to determine how many users you need in each variation.
Why is mobile A/B testing different from web?
Mobile apps have unique considerations: App Store review delays (2-5 days), slower user update adoption, higher variance in user behavior, and the need for session-based tracking instead of cookies. Our calculator includes mobile-specific presets and warnings.
What statistical significance level should I use?
95% confidence (ฮฑ = 0.05) is the industry standard. This means there's only a 5% chance your result is due to random chance. For high-stakes decisions, consider 99% confidence.
How long should I run a mobile A/B test?
At minimum, run for 7 days to capture weekly patterns (weekdays vs weekends). The exact duration depends on your daily active users and required sample size. Our Duration Estimator calculates this automatically.
What is minimum detectable effect (MDE)?
MDE is the smallest improvement you want to be able to detect. A 10% MDE means you want to detect if your variant improves conversion by at least 10% relative to the baseline. Smaller MDEs require larger sample sizes.
Can I run A/B tests without user tracking?
Yes! A/B testing requires counting events and unique sessions, not personal user profiles. Session-based analytics can power experiments without collecting PII or requiring persistent user IDs. This is exactly what Respectlytics is designed for.
Ready to Run Privacy-First Experiments?
Respectlytics tracks events and sessions โ everything you need for A/B testing โ without collecting personal data.