Approximate the probability that a Bayesian conversion experiment will cross a chosen posterior-probability threshold when the entered effect is real. The tool combines sample size, expected rates, and prior effective sample to provide a simulation-free planning estimate.
The main result estimates how often an experiment with the specified true rates would exceed the posterior decision threshold under a normal approximation.
This is not frequentist test power and is sensitive to prior shape and stopping policy; use simulation for final design decisions.
With expected rates of 10% and 11%, 5,000 new users per variant, no prior sample, and a 95% posterior threshold, approximate decision power is about 49%.
It uses the inputs, formula, units, and independence assumptions shown on this page. Change the inputs to match your own system or experiment.
Use representative averages for planning, preserve headroom where applicable, and test multiple plausible scenarios instead of relying on one point estimate.
No. The result is a planning estimate. Validate it with measured data, monitoring, load tests, or an experiment-design review as appropriate.
The calculator checks values required by the formula and displays an error instead of returning NaN or Infinity. Valid zero values remain available where they are meaningful.
Tools may use different statistical approximations, unit conventions, rounding rules, priors, confidence definitions, or operational headroom assumptions.
| Variable | Unit | Meaning |
|---|---|---|
| Effective n | users/variant | New sample plus prior effective sample |
| Signal | standard errors | Expected absolute difference divided by SE |
| Decision power | probability | Chance of crossing the posterior threshold |